Готові списки джерел за темами / Language transfer (Language learning) Germany

Зміст

  1. Статті в журналах
  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій
  6. Звіти організацій

Добірка наукової літератури з теми "Language transfer (Language learning) Germany"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями

Оберіть тип джерела:

Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Language transfer (Language learning) Germany".

Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.

Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.

Статті в журналах з теми "Language transfer (Language learning) Germany"

1

Jajić Novogradec, Marina. "Positive and Negative Lexical Transfer in English Vocabulary Acquisition." ELOPE: English Language Overseas Perspectives and Enquiries 18, no. 2 (December 29, 2021): 139–65. http://dx.doi.org/10.4312/elope.18.2.139-165.

Повний текст джерела
Анотація:
The aim of the paper is to explore the appearance of positive and negative lexical transfer of plurilingual learners in English vocabulary acquisition. Cross-linguistic influences in the study are examined by word translation tasks from Croatian into English, including true, partial, and deceptive cognates or false friends in English, German, and Italian. The results have revealed different language dominances and positive or negative transfer manifestation. Lexical transfer from L4 German is manifested positively, but the Italian language seems to play a dominant role in the acquisition of English vocabulary. The effect of Croatian is manifested both positively and negatively. The study has confirmed previous psycholinguistic studies on the complexity of lexical connections in plurilingual learners and the dynamic interaction of various learning-based factors, such as language recency, proficiency, exposure to languages, the order in which languages are learned, and the formal context in language learning.
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Sadouki, Fatiha. "Examples of cross-linguistic influence in learning German as a foreign language." EduLingua 6, no. 1 (2020): 61–83. http://dx.doi.org/10.14232/edulingua.2020.1.4.

Повний текст джерела
Анотація:
The present study sheds light on cross-linguistic influence and language transfer in third or additional language learning and explores the factors affecting the learning of third or additional language in a multilingual context. It aims at investigating the extent to which the typologically more similar language influences the language being learned. This study was carried out with the participation of 30 third-year students in the foreign languages stream at Al-Kawakibi Secondary School-Touggourt in Algeria. The participants had Arabic as L1, French as L2, English as L3 and they were learning L4 German. The instruments included two translation tasks and a paragraph writing in German, in addition to a questionnaire about learners' self-rated language proficiency of their non-native languages. The findings show that students tend to translate into the language which is typologically more similar to German, in this case English, that influences learning L4 German the most.
Стилі APA, Harvard, Vancouver, ISO та ін.
3

Sabourin, Laura, Laurie A. Stowe, and Ger J. de Haan. "Transfer effects in learning a second language grammatical gender system." Second Language Research 22, no. 1 (January 2006): 1–29. http://dx.doi.org/10.1191/0267658306sr259oa.

Повний текст джерела
Анотація:
In this article second language (L2) knowledge of Dutch grammatical gender is investigated. Adult speakers of German, English and a Romance language (French, Italian or Spanish) were investigated to explore the role of transfer in learning the Dutch grammatical gender system. In the first language (L1) systems, German is the most similar to Dutch coming from a historically similar system. The Romance languages have grammatical gender; however, the system is not congruent to the Dutch system. English does not have grammatical gender (although semantic gender is marked in the pronoun system). Experiment 1, a simple gender assignment task, showed that all L2 participants tested could assign the correct gender to Dutch nouns (all L2 groups performing on average above 80%), although having gender in the L1 did correlate with higher accuracy, particularly when the gender systems were very similar. Effects of noun familiarity and a default gender strategy were found for all participants. In Experiment 2 agreement between the noun and the relative pronoun was investigated. In this task a distinct performance hierarchy was found with the German group performing the best (though significantly worse than native speakers), the Romance group performing well above chance (though not as well as the German group), and the English group performing at chance. These results show that L2 acquisition of grammatical gender is affected more by the morphological similarity of gender marking in the L1 and L2 than by the presence of abstract syntactic gender features in the L1.
Стилі APA, Harvard, Vancouver, ISO та ін.
4

Jarosz, Józef. "Wirklichkeitsnah oder stereotyp? Das Bild von Dänemark und den Dänen in ausgewählten deutschen Lehrbüchern für Dänisch als Fremdsprache." Folia Scandinavica Posnaniensia 20, no. 1 (December 1, 2016): 91–104. http://dx.doi.org/10.1515/fsp-2016-0028.

Повний текст джерела
Анотація:
Abstract The contemporary teaching of foreign languages assumes the development of the ability to use a foreign language in different communication situations. Apart from language competence, also the cultural competence is developed as it is a necessary component of communication. A successful transfer of knowledge and language skills in the process of foreign language learning is determined by a textbook (in addition to other factors). The goal of this article is to analyze the content and assess three Danish textbooks, which were published in Germany in the years 2008-2010. The textbooks are examined in terms of knowledge about Danish life and institutions, the transfer of intercultural competence and the presence of stereotypes. The textbooks were studied based on the list of criteria and it resulted in stating that the textbooks fulfill the objective of providing the knowledge about the country to a great degree. The intercultural component and the issue of stereotypes are dealt with in a different manner.
Стилі APA, Harvard, Vancouver, ISO та ін.
5

Sokolova, M., and E. Plisov. "CROSS-LINGUISTIC TRANSFER CLASSROOM L3 ACQUISITION IN UNIVERSITY SETTING." Vestnik of Minin University 7, no. 1 (March 17, 2019): 6. http://dx.doi.org/10.26795/2307-1281-2019-7-1-6.

Повний текст джерела
Анотація:
Introduction: the paper investigates cross-linguistic influences between the two previously learnt languages and their effects on classroom L3 acquisition. The study checks the predictions of the existing theories of mechanisms of transfer into the L3 attested for naturalistic learners. The main predictions get confirmed with the population of classroom learners of English as the L3. All the participants are native speakers of Russian. They all learnt their dominant foreign language, either French or German, in the classroom. The results suggest a governing role of the Universal Grammar in classroom language learning. Materials and Methods: the experiment uses three production tasks: written production, oral production and pronunciation task. The written assignment asks the participants to translate sentences from Russian into English. The target sentence contains the existential there are that does not exist in Russian. The way the participants structure the target sentence in English allows for conclusion about possible influences of the first foreign language on the development of their L3- English. In the oral production task, the participants are prompted to produce negative sentences. The influences from previously learnt languages is traced through the placement of the negation not. In the pronunciation task Praat was used to measure the duration and the formant frequency of the nasal [N] in English. Differences in sound quality trace back to the influences from the previously learnt languages. The data were analyzed with one-way ANOVA for between and within group differences. Results: in the written task, the participants who studied German as their first foreign language prefer verb final placement in the subordinate, which is ungrammatical in English but grammatical in German. The L2-French group put the verb in the right place, but they do not use the existential there are, which required in English. In the oral task, the placement of negation is Russian-like in both groups. In pronunciation, the quality of English [N] is influenced by the amount of nasality the participants learnt before, i.e. French influences make the English [N] more nasalized than the [N] in the group with German as the first foreign language. Discussion and Conclusion: classroom learners of English as the L3 experience influences from all the previously learnt languages, the native language and the first foreign language. These findings pattern with the assumptions of the main generative theories of naturalistic L3 acquisition. Concluding that classroom language learning is governed by universal grammar, the teaching can benefit from predicting what cross-linguistic influences can be facilitative or not for the acquisition of the target language.
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Bawej, Izabela. "Rozumowanie dedukcyjne w procesie uczenia się języka niemieckiego jako drugiego języka obcego na przykładzie podsystemu gramatycznego." Neofilolog, no. 58/1 (April 27, 2022): 85–98. http://dx.doi.org/10.14746/n.2022.58.1.6.

Повний текст джерела
Анотація:
The role of the first foreign language in second foreign language learning is an interesting research question. The main purpose of the research was to relate if and how the learners make deductions about German grammar based on English language skills. Therefore, this study presents the results of a survey conducted among students of Applied Linguistics who learn German after English. Participants were interviewed to state their opinion about the usefulness of English in learning German structures. The results of this inquiry allow the conclusions that learners use and transfer the previously acquired knowledge and information from what they have in first foreign language in order to understand, learn or form structures in the second foreign language. They compare both languages, look for similarities in the creation of the construction and the application of the structures or constructions, conclude by analogies between English and German in grammatical subsystem. In this way they deduce that English makes possible and facilitates to memorize grammatical forms while learning German, e.g. passive voice, articles, tenses, irregular verbs, comparative and superlative adjectives.
Стилі APA, Harvard, Vancouver, ISO та ін.
7

O'BRIEN, MARY GRANTHAM, CARRIE N. JACKSON, and CHRISTINE E. GARDNER. "Cross-linguistic differences in prosodic cues to syntactic disambiguation in German and English." Applied Psycholinguistics 35, no. 1 (August 10, 2012): 27–70. http://dx.doi.org/10.1017/s0142716412000252.

Повний текст джерела
Анотація:
ABSTRACTThis study examined whether late-learning English–German second language (L2) learners and late-learning German–English L2 learners use prosodic cues to disambiguate temporarily ambiguous first language and L2 sentences during speech production. Experiments 1a and 1b showed that English–German L2 learners and German–English L2 learners used a pitch rise and pitch accent to disambiguate PP-attachment sentences in German. However, the same participants, as well as monolingual English speakers, only used pitch accent to disambiguate similar English sentences. Taken together, these results indicate the L2 learners used prosody to disambiguate sentences in both of their languages and did not fully transfer cues to disambiguation from their first language to their L2. The results have implications for the acquisition of L2 prosody and the interaction between prosody and meaning in L2 production.
Стилі APA, Harvard, Vancouver, ISO та ін.
8

Božinović, Nikolina, and Barbara Perić. "The role of typology and formal similarity in third language acquisition (German and Spanish)." Strani jezici 50, no. 1 (2021): 9–30. http://dx.doi.org/10.22210/strjez/50-1/1.

Повний текст джерела
Анотація:
The focus of this study is the role of previously acquired languages in the acquisition of a third language (L3). It is focused on cross-linguistic influences (CLI) in German/Spanish third lan- guage acquisition (TLA) by learners with Croatian first language (L1) and English second language (L2). Participants in this study were third-year undergraduate students at Roch- ester Institute of Technology’s subsidiary in Croatia (RIT Croatia). All the participants had exclusively Croatian as L1, English as L2, and were learning German and Spanish as L3 at the time of the study. The present study investigates the relationship between language typology and formal similarity and transfer/error production, since many studies have demonstrated that typology plays a determining role in cross-linguistic transfer (Cenoz, Hufeisen & Jess- ner 2001; Hammarberg 2001; Rothman 2010). There are various areas of similarity and dis- similarity between Croatian, English, German, and Spanish. A significant portion of English vocabulary comes from Romance and Latinate sources. Due to these facts, we argue that the strongest L2 (English) influence will be found in the area of lexicon. On the other hand, Cro- atian, German, and Spanish are more similar in the area of morphology, due to the fact that these languages have a higher degree of inflection than English. Accordingly, we argue that the strongest L1 (Croatian) influence will be found in the area of morphology. The results of this research confirmed our initial hypothesis that the type of transfer episodes observed may be related to language typology and formal similarity between specific features of languages. Similarities at the level of lexis and grammar between L2 English and L3 German and Spanish can influence the acquisition process of German and Spanish.
Стилі APA, Harvard, Vancouver, ISO та ін.
9

Hopp, Holger. "Cross-linguistic influence in the child third language acquisition of grammar: Sentence comprehension and production among Turkish-German and German learners of English." International Journal of Bilingualism 23, no. 2 (January 24, 2018): 567–83. http://dx.doi.org/10.1177/1367006917752523.

Повний текст джерела
Анотація:
Aims and Objectives/Purpose/Research Questions: This paper investigates the extent to which current formal models of third language (L3) grammatical acquisition extend to sequential child L3 acquisition. We examine whether heritage speakers learning a foreign language as an early L3 transfer grammatical properties from the heritage language or the dominant second language (L2). Design/Methodology/Approach: We used a sentence repetition task and a picture story retelling task. The tasks focussed on grammatical phenomena that were either different between English and German, that is, verb-second and adverb order, or between English and German, on the one hand, and Turkish, on the other, that is, verb-complement order as well as subject and article realization. Data and Analysis: We tested matched groups of 31 Turkish-German and 31 monolingual German children learning English in grades 3 and 4, and we compared sentence repetitions as well as oral sentence production across different grammatical phenomena using parametric statistics. Findings/Conclusions: In both tasks, the two groups perform indistinguishably from each other, and both groups show selective transfer of grammatical properties from German. These findings suggest L2 transfer from a typologically related language in sequential child L3 acquisition. Originality: This paper breaks new ground by testing the applicability of formal models of adult L3 acquisition of grammar to sequential child L3 learners. It uses aural comprehension and oral production tasks with carefully matched groups of L2 and L3 learners of English to isolate the source of grammatical transfer in L3 acquisition. Significance/Implications: The research advances our understanding of cross-linguistic influence and unravels the dynamics of grammatical transfer in early child multilingualism. It adjudicates between current models of transfer in L3 acquisition in a multiple-methods design, it shows that these models apply to early L3 acquisition of heritage speakers, and it highlights that these models need to be expanded to include factors such as dominance and proficiency in prior languages.
Стилі APA, Harvard, Vancouver, ISO та ін.
10

Odaryuk, Irina V., and Artem S. Gampartsumov. "Development of foreign language communicative competence in the process of academic and professional interaction in a second foreign language." Samara Journal of Science 9, no. 3 (November 20, 2020): 282–86. http://dx.doi.org/10.17816/snv202093307.

Повний текст джерела
Анотація:
This paper examines the peculiarities of teaching German as a second foreign language in a railway university. The analysis emphasizes the inefficiency of traditional methods and the success of the bilingual approach, which consists in a harmonious combination of methodological principles of teaching the first and second foreign languages. The authors carry out a theoretical analysis of the fundamental principles of teaching a second foreign language: a comparative approach, the principle of reliance on the first foreign and native languages, an autonomous approach, a cognitive principle. The paper deals with the issues related to interference and transfer in teaching a second foreign language. Project methods (simulation, presentation speech, Lapbook-technology) tested by the author in the learning process are offered as learning technologies, the use of which facilitates effective mastering of foreign language skills and abilities. The syllabus of the course A Second Foreign language developed by teachers of the Rostov State Transport University in accordance with the new edition of the Federal State Educational Standard is analyzed. The conclusion is made that this syllabus satisfies the requirements put forward by methodologists to the process of teaching a second foreign language. The analysis of the organization of the educational process with the use of textbooks and a fund of assessment tools prepared for the course is expected to be the subject of our further research.
Стилі APA, Harvard, Vancouver, ISO та ін.
Більше джерел

Дисертації з теми "Language transfer (Language learning) Germany"

1

Samperio, Sanchez Nahum. "General learning strategies : identification, transfer to language learning and effect on language achievement." Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/412008/.

Повний текст джерела
Анотація:
Each learner has a set repertoire of general learning strategies that he or she uses despite the learning context. The purpose of this study is to identify the general learning strategies that beginner learners of English have in their repertoire, the transfer of such strategies to language learning and the predictive value they have in language achievement. It is also intended to discover the effect that the teaching of not frequently used general learning strategies have on learners’ language achievement. Additionally, to identify possible differences in strategy types and frequency of strategy use in low and high strategy users as well as high and low achievers of beginner English language learners. This study followed a mixed-methods research methodology by collecting numerical data by means of a 51-item general strategies questionnaire (Martinez- Guerrero 2004) applied in two administrations. The sample consists of 118 beginner English language learners in a language center at a northern Mexican University. Data were analyzed with the SPSS and Excel software. The qualitative data was collected through twenty individual semistructured interviews; furthermore, three one-hour-forty minute strategy instruction sessions were included as the treatment. Quantitative results show that learners have a more frequent use of Achievement Motivation, Cognitive and Concentration strategies; and less frequent use of Study, Study Organization, and Interaction in Class strategies. Qualitative findings indicate that learners use Study and Study organization and Concentration strategies largely in both general learning and language learning. Qualitative data complement and extend the quantitative data gathered in the questionnaire. No significant differences were found on the type of strategies that learners use in general learning contexts and language learning, which suggests that learners transfer their learning strategies from their general strategy repertoire to language learning as the first tools to deal with language learning tasks. A positive correlation was found between learning strategy use and language achievement test scores. Achievement test scores were primarily predicted by the use of Achievement Motivation and Interaction in Class strategies, and to a lesser extent by affective and study strategies. Strategy instruction sessions had no significant increase in the adoption and use of strategies. Furthermore, high and low achievers and strategy users seem to use the same type of strategies; the frequency of strategy use and how they use the strategy represented the difference between types of learners. Finally, a number of language learning strategies emerge from qualitative data that learners use in language learning. Pedagogical implications of the findings of this study provide a potential framework to help not only teachers but also institutions in identifying and teaching new and specific learning strategies.
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Zhang, Yuan Ph D. Massachusetts Institute of Technology. "Transfer learning for low-resource natural language analysis." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/108847.

Повний текст джерела
Анотація:
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 131-142).
Expressive machine learning models such as deep neural networks are highly effective when they can be trained with large amounts of in-domain labeled training data. While such annotations may not be readily available for the target task, it is often possible to find labeled data for another related task. The goal of this thesis is to develop novel transfer learning techniques that can effectively leverage annotations in source tasks to improve performance of the target low-resource task. In particular, we focus on two transfer learning scenarios: (1) transfer across languages and (2) transfer across tasks or domains in the same language. In multilingual transfer, we tackle challenges from two perspectives. First, we show that linguistic prior knowledge can be utilized to guide syntactic parsing with little human intervention, by using a hierarchical low-rank tensor method. In both unsupervised and semi-supervised transfer scenarios, this method consistently outperforms state-of-the-art multilingual transfer parsers and the traditional tensor model across more than ten languages. Second, we study lexical-level multilingual transfer in low-resource settings. We demonstrate that only a few (e.g., ten) word translation pairs suffice for an accurate transfer for part-of-speech (POS) tagging. Averaged across six languages, our approach achieves a 37.5% improvement over the monolingual top-performing method when using a comparable amount of supervision. In the second monolingual transfer scenario, we propose an aspect-augmented adversarial network that allows aspect transfer over the same domain. We use this method to transfer across different aspects in the same pathology reports, where traditional domain adaptation approaches commonly fail. Experimental results demonstrate that our approach outperforms different baselines and model variants, yielding a 24% gain on this pathology dataset.
by Yuan Zhang.
Ph. D.
Стилі APA, Harvard, Vancouver, ISO та ін.
3

Jin, Di Ph D. Massachusetts Institute of Technology. "Transfer learning and robustness for natural language processing." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129004.

Повний текст джерела
Анотація:
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 189-217).
Teaching machines to understand human language is one of the most elusive and long-standing challenges in Natural Language Processing (NLP). Driven by the fast development of deep learning, state-of-the-art NLP models have already achieved human-level performance in various large benchmark datasets, such as SQuAD, SNLI, and RACE. However, when these strong models are deployed to real-world applications, they often show poor generalization capability in two situations: 1. There is only a limited amount of data available for model training; 2. Deployed models may degrade significantly in performance on noisy test data or natural/artificial adversaries. In short, performance degradation on low-resource tasks/datasets and unseen data with distribution shifts imposes great challenges to the reliability of NLP models and prevent them from being massively applied in the wild. This dissertation aims to address these two issues.
Towards the first one, we resort to transfer learning to leverage knowledge acquired from related data in order to improve performance on a target low-resource task/dataset. Specifically, we propose different transfer learning methods for three natural language understanding tasks: multi-choice question answering, dialogue state tracking, and sequence labeling, and one natural language generation task: machine translation. These methods are based on four basic transfer learning modalities: multi-task learning, sequential transfer learning, domain adaptation, and cross-lingual transfer. We show experimental results to validate that transferring knowledge from related domains, tasks, and languages can improve the target task/dataset significantly. For the second issue, we propose methods to evaluate the robustness of NLP models on text classification and entailment tasks.
On one hand, we reveal that although these models can achieve a high accuracy of over 90%, they still easily crash over paraphrases of original samples by changing only around 10% words to their synonyms. On the other hand, by creating a new challenge set using four adversarial strategies, we find even the best models for the aspect-based sentiment analysis task cannot reliably identify the target aspect and recognize its sentiment accordingly. On the contrary, they are easily confused by distractor aspects. Overall, these findings raise great concerns of robustness of NLP models, which should be enhanced to ensure their long-run stable service.
by Di Jin.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Mechanical Engineering
Стилі APA, Harvard, Vancouver, ISO та ін.
4

Utgof, Darja. "The Perception of Lexical Similarities Between L2 English and L3 Swedish." Thesis, Linköping University, Department of Culture and Communication, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-15874.

Повний текст джерела
Анотація:

The present study investigates lexical similarity perceptions by students of Swedish as a foreign language (L3) with a good yet non-native proficiency in English (L2). The general theoretical framework is provided by studies in transfer of learning and its specific instance, transfer in language acquisition.

It is accepted as true that all previous linguistic knowledge is facilitative in developing proficiency in a new language. However, a frequently reported phenomenon is that students see similarities between two systems in a different way than linguists and theoreticians of education do. As a consequence, the full facilitative potential of transfer remains unused.

The present research seeks to shed light on the similarity perceptions with the focus on the comprehension of a written text. In order to elucidate students’ views, a form involving similarity judgements and multiple choice questions for formally similar items has been designed, drawing on real language use as provided by corpora. 123 forms have been distributed in 6 groups of international students, 4 of them studying Swedish at Level I and 2 studying at Level II. 

The test items in the form vary in the degree of formal, semantic and functional similarity from very close cognates, to similar words belonging to different word classes, to items exhibiting category membership and/or being in subordinate/superordinate relation to each other, to deceptive cognates. The author proposes expected similarity ratings and compares them to the results obtained. The objective measure of formal similarity is provided by a string matching algorithm, Levenshtein distance.

The similarity judgements point at the fact that intermediate similarity values can be considered problematic. Similarity ratings between somewhat similar items are usually lower than could be expected. Besides, difference in grammatical meaning lowers similarity values significantly even if lexical meaning nearly coincides. Thus, the obtained results indicate that in order to utilize similarities to facilitate language learning, more attention should be paid to underlying similarities.

Стилі APA, Harvard, Vancouver, ISO та ін.
5

Casula, Camilla. "Transfer Learning for Multilingual Offensive Language Detection with BERT." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412450.

Повний текст джерела
Анотація:
The popularity of social media platforms has led to an increase in user-generated content being posted on the Internet. Users, masked behind what they perceive as anonymity, can express offensive and hateful thoughts on these platforms, creating a need to detect and filter abusive content. Since the amount of data available on the Internet is impossible to analyze manually, automatic tools are the most effective choice for detecting offensive and abusive messages. Academic research on the detection of offensive language on social media has been on the rise in recent years, with more and more shared tasks being organized on the topic. State-of-the-art deep-learning models such as BERT have achieved promising results on offensive language detection in English. However, multilingual offensive language detection systems, which focus on several languages at once, have remained underexplored until recently. In this thesis, we investigate whether transfer learning can be useful for improving the performance of a classifier for detecting offensive speech in Danish, Greek, Arabic, Turkish, German, and Italian. More specifically, we first experiment with using machine-translated data as input to a classifier. This allows us to evaluate whether machine translated data can help classification. We then experiment with fine-tuning multiple pre-trained BERT models at once. This parallel fine-tuning process, named multi-channel BERT (Sohn and Lee, 2019), allows us to exploit cross-lingual information with the goal of understanding its impact on the detection of offensive language. Both the use of machine translated data and the exploitation of cross-lingual information could help the task of detecting offensive language in cases in which there is little or no annotated data available, for example for low-resource languages. We find that using machine translated data, either exclusively or mixed with gold data, to train a classifier on the task can often improve its performance. Furthermore, we find that fine-tuning multiple BERT models in parallel can positively impact classification, although it can lead to robustness issues for some languages.
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Panzeri-Alvarez, Christina. "Metacognition and language transfer for an English language development transitional program." CSUSB ScholarWorks, 1998. https://scholarworks.lib.csusb.edu/etd-project/1780.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
7

Tse, Siu-ching, and 謝兆政. "Cross linguistic influence in polyglots: encoding of the future by L3 learners of Swedish." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B4842187X.

Повний текст джерела
Анотація:
The current study aims to investigate the source(s)of cross linguistic influence(CLI)on the production of future encoding strategies by L1 Cantonese learners of L3 Swedish who speak L2 English. In the literature of third language acquisition (TLA) research, the language status of native and non-native languages as well as genetic and (psycho)typological language distance are identified to be important to TLA processes but the current knowledge is insufficient to inform which factor(s) is/are more influential. Given the close genetic distance between English and Swedish and the status of English as a second language, it is hypothesized that CLI on L3 Swedish comes from L2 English rather than L1 Cantonese. Any confirmation or rejection to this hypothesis serves to inform the relationship of language status and language distance to TLA. To test this hypothesis, linguistic background questionnaire and a picture elicitation task are designed to record the production of future ideas in the three languages. Through qualitative and quantitative analyses, mixed sources of CLI from Cantonese and English are identified. An equidistance representation of non-native languages is also identified in which non-native English and Swedish respectively show similar degree of cross linguistic matching in relation to native Cantonese regardless which of them is the principal source of CLI. The hypothesis of differentiation of linguistic representation in the minds of polyglots is therefore proposed and further verification and investigation is required.
published_or_final_version
Linguistics
Master
Master of Arts
Стилі APA, Harvard, Vancouver, ISO та ін.
8

Meftah, Sara. "Neural Transfer Learning for Domain Adaptation in Natural Language Processing." Thesis, université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG021.

Повний текст джерела
Анотація:
Les méthodes d’apprentissage automatique qui reposent sur les Réseaux de Neurones (RNs) ont démontré des performances de prédiction qui s'approchent de plus en plus de la performance humaine dans plusieurs applications du Traitement Automatique de la Langue (TAL) qui bénéficient de la capacité des différentes architectures des RNs à généraliser à partir des régularités apprises à partir d'exemples d'apprentissage. Toutefois, ces modèles sont limités par leur dépendance aux données annotées. En effet, pour être performants, ces modèles neuronaux ont besoin de corpus annotés de taille importante. Par conséquent, uniquement les langues bien dotées peuvent bénéficier directement de l'avancée apportée par les RNs, comme par exemple les formes formelles des langues. Dans le cadre de cette thèse, nous proposons des méthodes d'apprentissage par transfert neuronal pour la construction d'outils de TAL pour les langues peu dotées en exploitant leurs similarités avec des langues bien dotées. Précisément, nous expérimentons nos approches pour le transfert à partir du domaine source des textes formels vers le domaine cible des textes informels (langue utilisée dans les réseaux sociaux). Tout au long de cette thèse nous proposons différentes contributions. Tout d'abord, nous proposons deux approches pour le transfert des connaissances encodées dans les représentations neuronales d'un modèle source, pré-entraîné sur les données annotées du domaine source, vers un modèle cible, adapté par la suite sur quelques exemples annotés du domaine cible. La première méthode transfère des représentations contextuelles pré-entraînées sur le domaine source. Tandis que la deuxième méthode utilise des poids pré-entraînés pour initialiser les paramètres du modèle cible. Ensuite, nous effectuons une série d'analyses pour repérer les limites des méthodes proposées ci-dessus. Nous constatons que, même si l'approche d'apprentissage par transfert proposée améliore les résultats du domaine cible, un transfert négatif « dissimulé » peut atténuer le gain final apporté par l'apprentissage par transfert. De plus, une analyse interprétative du modèle pré-entraîné, montre que les neurones pré-entraînés peuvent être biaisés par ce qu'ils ont appris du domaine source, et donc peuvent avoir des difficultés à apprendre des « patterns » spécifiques au domaine cible. Issu de notre analyse, nous proposons un nouveau schéma d'adaptation qui augmente le modèle cible avec des neurones normalisés, pondérés et initialisés aléatoirement qui permettent une meilleure adaptation au domaine cible tout en conservant les connaissances apprises du domaine source. Enfin, nous proposons une approche d’apprentissage par transfert qui permet de profiter des similarités entre différentes tâches, en plus des connaissances pré-apprises du domaine source
Recent approaches based on end-to-end deep neural networks have revolutionised Natural Language Processing (NLP), achieving remarkable results in several tasks and languages. Nevertheless, these approaches are limited with their "gluttony" in terms of annotated data, since they rely on a supervised training paradigm, i.e. training from scratch on large amounts of annotated data. Therefore, there is a wide gap between NLP technologies capabilities for high-resource languages compared to the long tail of low-resourced languages. Moreover, NLP researchers have focused much of their effort on training NLP models on the news domain, due to the availability of training data. However, many research works have highlighted that models trained on news fail to work efficiently on out-of-domain data, due to their lack of robustness against domain shifts. This thesis presents a study of transfer learning approaches, through which we propose different methods to take benefit from the pre-learned knowledge on the high-resourced domain to enhance the performance of neural NLP models in low-resourced settings. Precisely, we apply our approaches to transfer from the news domain to the social media domain. Indeed, despite the importance of its valuable content for a variety of applications (e.g. public security, health monitoring, or trends highlight), this domain is still poor in terms of annotated data. We present different contributions. First, we propose two methods to transfer the knowledge encoded in the neural representations of a source model pretrained on large labelled datasets from the source domain to the target model, further adapted by a fine-tuning on few annotated examples from the target domain. The first transfers contextualised supervisedly pretrained representations, while the second method transfers pretrained weights, used to initialise the target model's parameters. Second, we perform a series of analysis to spot the limits of the above-mentioned proposed methods. We find that even if the proposed transfer learning approach enhances the performance on social media domain, a hidden negative transfer may mitigate the final gain brought by transfer learning. In addition, an interpretive analysis of the pretrained model, show that pretrained neurons may be biased by what they have learned from the source domain, thus struggle with learning uncommon target-specific patterns. Third, stemming from our analysis, we propose a new adaptation scheme which augments the target model with normalised, weighted and randomly initialised neurons that beget a better adaptation while maintaining the valuable source knowledge. Finally, we propose a model, that in addition to the pre-learned knowledge from the high-resource source-domain, takes advantage of various supervised NLP tasks
Стилі APA, Harvard, Vancouver, ISO та ін.
9

Mozafari, Marzieh. "Hate speech and offensive language detection using transfer learning approaches." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAS007.

Повний текст джерела
Анотація:
Une des promesses des plateformes de réseaux sociaux (comme Twitter et Facebook) est de fournir un endroit sûr pour que les utilisateurs puissent partager leurs opinions et des informations. Cependant, l’augmentation des comportements abusifs, comme le harcèlement en ligne ou la présence de discours de haine, est bien réelle. Dans cette thèse, nous nous concentrons sur le discours de haine, l'un des phénomènes les plus préoccupants concernant les réseaux sociaux.Compte tenu de sa forte progression et de ses graves effets négatifs, les institutions, les plateformes de réseaux sociaux et les chercheurs ont tenté de réagir le plus rapidement possible. Les progrès récents des algorithmes de traitement automatique du langage naturel (NLP) et d'apprentissage automatique (ML) peuvent être adaptés pour développer des méthodes automatiques de détection des discours de haine dans ce domaine.Le but de cette thèse est d'étudier le problème du discours de haine et de la détection des propos injurieux dans les réseaux sociaux. Nous proposons différentes approches dans lesquelles nous adaptons des modèles avancés d'apprentissage par transfert (TL) et des techniques de NLP pour détecter automatiquement les discours de haine et les contenus injurieux, de manière monolingue et multilingue.La première contribution concerne uniquement la langue anglaise. Tout d'abord, nous analysons le contenu textuel généré par les utilisateurs en introduisant un nouveau cadre capable de catégoriser le contenu en termes de similarité basée sur différentes caractéristiques. En outre, en utilisant l'API Perspective de Google, nous mesurons et analysons la « toxicité » du contenu. Ensuite, nous proposons une approche TL pour l'identification des discours de haine en utilisant une combinaison du modèle non supervisé pré-entraîné BERT (Bidirectional Encoder Representations from Transformers) et de nouvelles stratégies supervisées de réglage fin. Enfin, nous étudions l'effet du biais involontaire dans notre modèle pré-entraîné BERT et proposons un nouveau mécanisme de généralisation dans les données d'entraînement en repondérant les échantillons puis en changeant les stratégies de réglage fin en termes de fonction de perte pour atténuer le biais racial propagé par le modèle. Pour évaluer les modèles proposés, nous utilisons deux datasets publics provenant de Twitter.Dans la deuxième contribution, nous considérons un cadre multilingue où nous nous concentrons sur les langues à faibles ressources dans lesquelles il n'y a pas ou peu de données annotées disponibles. Tout d'abord, nous présentons le premier corpus de langage injurieux en persan, composé de 6 000 messages de micro-blogs provenant de Twitter, afin d'étudier la détection du langage injurieux. Après avoir annoté le corpus, nous réalisons étudions les performances des modèles de langages pré-entraînés monolingues et multilingues basés sur des transformeurs (par exemple, ParsBERT, mBERT, XLM-R) dans la tâche en aval. De plus, nous proposons un modèle d'ensemble pour améliorer la performance de notre modèle. Enfin, nous étendons notre étude à un problème d'apprentissage multilingue de type " few-shot ", où nous disposons de quelques données annotées dans la langue cible, et nous adaptons une approche basée sur le méta-apprentissage pour traiter l'identification des discours de haine et du langage injurieux dans les langues à faibles ressources
The great promise of social media platforms (e.g., Twitter and Facebook) is to provide a safe place for users to communicate their opinions and share information. However, concerns are growing that they enable abusive behaviors, e.g., threatening or harassing other users, cyberbullying, hate speech, racial and sexual discrimination, as well. In this thesis, we focus on hate speech as one of the most concerning phenomenon in online social media.Given the high progression of online hate speech and its severe negative effects, institutions, social media platforms, and researchers have been trying to react as quickly as possible. The recent advancements in Natural Language Processing (NLP) and Machine Learning (ML) algorithms can be adapted to develop automatic methods for hate speech detection in this area.The aim of this thesis is to investigate the problem of hate speech and offensive language detection in social media, where we define hate speech as any communication criticizing a person or a group based on some characteristics, e.g., gender, sexual orientation, nationality, religion, race. We propose different approaches in which we adapt advanced Transfer Learning (TL) models and NLP techniques to detect hate speech and offensive content automatically, in a monolingual and multilingual fashion.In the first contribution, we only focus on English language. Firstly, we analyze user-generated textual content to gain a brief insight into the type of content by introducing a new framework being able to categorize contents in terms of topical similarity based on different features. Furthermore, using the Perspective API from Google, we measure and analyze the toxicity of the content. Secondly, we propose a TL approach for identification of hate speech by employing a combination of the unsupervised pre-trained model BERT (Bidirectional Encoder Representations from Transformers) and new supervised fine-tuning strategies. Finally, we investigate the effect of unintended bias in our pre-trained BERT based model and propose a new generalization mechanism in training data by reweighting samples and then changing the fine-tuning strategies in terms of the loss function to mitigate the racial bias propagated through the model. To evaluate the proposed models, we use two publicly available datasets from Twitter.In the second contribution, we consider a multilingual setting where we focus on low-resource languages in which there is no or few labeled data available. First, we present the first corpus of Persian offensive language consisting of 6k micro blog posts from Twitter to deal with offensive language detection in Persian as a low-resource language in this domain. After annotating the corpus, we perform extensive experiments to investigate the performance of transformer-based monolingual and multilingual pre-trained language models (e.g., ParsBERT, mBERT, XLM-R) in the downstream task. Furthermore, we propose an ensemble model to boost the performance of our model. Then, we expand our study into a cross-lingual few-shot learning problem, where we have a few labeled data in target language, and adapt a meta-learning based approach to address identification of hate speech and offensive language in low-resource languages
Стилі APA, Harvard, Vancouver, ISO та ін.
10

Mau, Pui-sze Priscilla. "Cross-language transfer of phonological awareness in Chinese-English bilinguals." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B36889301.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Більше джерел

Книги з теми "Language transfer (Language learning) Germany"

1

Rosén, Christina. "Warum klingt das nicht deutsch?": Probleme der Informationsstrukturierung in deutschen Texten schwedischer Schüler und Studenten. Stockholm: Almqvist & Wiksell, 2006.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Hansen-Jaax, Dörte. Transfer bei Diglossie: Synchrone Sprachkontaktphänomene im Niederdeutschen. Hamburg: Kovač, 1995.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
3

Schloter, Andreas Leonhard. Interferenzfehler beim Erwerb des Englischen als Fremdsprache: Ein empirischer Beitrag zur Fehlerursachenforschung. München: Tuduv, 1992.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
4

Befähigung zum zusammenhängenden (monologischen) Sprechen durch Transfer der Schreibtätigkeit im Russischunterricht der allgemeinbildenden Schule. Frankfurt am Main: P. Lang, 1994.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
5

Leontiy, Halyna. Multikulturelles Deutschland im Sprachvergleich: Das Deutsche im Fokus der meist verbreiteten Migrantensprachen : ein Handbuch für DaF-Lehrende und Studierende, für Pädagogen/-innen und Erzieher/-innen. Berlin: Lit, 2013.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Gass, Susan M., and Larry Selinker, eds. Language Transfer in Language Learning. Amsterdam: John Benjamins Publishing Company, 1992. http://dx.doi.org/10.1075/lald.5.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
7

M, Gass Susan, and Selinker Larry 1937-, eds. Language transfer in language learning. Amsterdam: J. Benjamins Pub. Co., 1992.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
8

M, Gass Susan, and Selinker Larry 1937-, eds. Language transfer in language learning. Amsterdam: J. Benjamins Pub. Co., 1994.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
9

Bordag, Denisa. Psycholinguistische Aspekte der Interferenzerscheinungen in der Flexionsmorphologie des Tschechischen als Fremdsprache. Hildesheim: Olms, 2006.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
10

Odlin, Terence. Language transfer: Cross-linguistic influence in language learning. Cambridge: Cambridge University Press, 1989.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Більше джерел

Частини книг з теми "Language transfer (Language learning) Germany"

1

Angelovska, Tanja, and Angela Hahn. "Written L3 (English): Transfer Phenomena of L2 (German) Lexical and Syntactic Properties." In Second Language Learning and Teaching, 23–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29557-7_2.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Biswas, Rajarshi, Michael Barz, Mareike Hartmann, and Daniel Sonntag. "Improving German Image Captions Using Machine Translation and Transfer Learning." In Statistical Language and Speech Processing, 3–14. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89579-2_1.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
3

Janik, Marta Olga. "5. Positive and Negative Transfer in the L2 Adjective Inflection of English-, German- and Polish-speaking Learners of L2 Norwegian." In Crosslinguistic Influence and Distinctive Patterns of Language Learning, edited by Anne Golden, Scott Jarvis, and Kari Tenfjord, 84–109. Bristol, Blue Ridge Summit: Multilingual Matters, 2017. http://dx.doi.org/10.21832/9781783098774-007.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
4

Broselow, Ellen. "Nonobvious Transfer." In Language Transfer in Language Learning, 71. Amsterdam: John Benjamins Publishing Company, 1992. http://dx.doi.org/10.1075/lald.5.07bro.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
5

Zierdt, M., N. I. Bykov, D. Kley, A. A. Bondarovich, and G. Schmidt. "Technology Learning and Transfer of Knowledge—Practices and Lessons Learned from the German-Language Study Programme in Barnaul." In KULUNDA: Climate Smart Agriculture, 501–6. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15927-6_38.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Ard, Josh, and Taco Homburg. "Verification of Language Transfer." In Language Transfer in Language Learning, 47. Amsterdam: John Benjamins Publishing Company, 1992. http://dx.doi.org/10.1075/lald.5.06ard.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
7

Selinker, Larry, and Usha Lakshmanan. "Language Transfer And Fossilization." In Language Transfer in Language Learning, 197. Amsterdam: John Benjamins Publishing Company, 1992. http://dx.doi.org/10.1075/lald.5.13sel.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
8

Gass, Susan M., and Larry Selinker. "Introduction." In Language Transfer in Language Learning, 1. Amsterdam: John Benjamins Publishing Company, 1992. http://dx.doi.org/10.1075/lald.5.03gas.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
9

Corder, S. Pit. "A Role for the Mother Tongue." In Language Transfer in Language Learning, 18. Amsterdam: John Benjamins Publishing Company, 1992. http://dx.doi.org/10.1075/lald.5.04cor.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
10

Schachter, Jacquelyn. "A New Account of Language Transfer." In Language Transfer in Language Learning, 32. Amsterdam: John Benjamins Publishing Company, 1992. http://dx.doi.org/10.1075/lald.5.05sch.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.

Тези доповідей конференцій з теми "Language transfer (Language learning) Germany"

1

Gebhard, Christian Alexander. "Who attends our foreign language courses? A preliminary look into the profile of learners of Chinese." In 4th International Conference. Business Meets Technology. València: Editorial Universitat Politècnica de València, 2022. http://dx.doi.org/10.4995/bmt2022.2022.15328.

Повний текст джерела
Анотація:
This article takes a look into the profile of students enrolled at elective foreign language (FL) courses at German universities. Two surveys on their study biography show that learners of Chinese have on average learned more previous foreign languages than learners of Spanish. As more experienced FL learners, they draw on more FL learning strategies and more sources for transfer, a psycholinguistic process observed in FL learning. Based on contrastive theories, possible sources for transfer into and out of Chinese are suggested to contribute to the successful teaching of Chinese.
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Gimenez Calpe, Ana. "The Lecture-Performance: Implementing Performative Pedagogy in Literature Class." In Sixth International Conference on Higher Education Advances. Valencia: Universitat Politècnica de València, 2020. http://dx.doi.org/10.4995/head20.2020.11186.

Повний текст джерела
Анотація:
In recent years the performative has gained importance within the pedagogical field and has opened new perspectives in educational research. Experience has shown that the integration of performative elements in the learning process allow teachers to involve learners emotionally and cognitively. The present paper deals with a learning experience performed with students in the course “German Literature (2nd language)” at the University of Valencia. From the perspective of Performative Pedagogy, students are asked to carry out a research project and then transfer the acquired knowledge to the theatrical format that must be didactic: a Lecture-Performance. This activity highlights the benefits of students’ autonomous and cooperative learning, as well as the development of students’ performative competence, with which they achieved deeper levels of understanding and improved their retention of what was studied. The teacher evaluation and a questionnaire carried out by the students at the end of the activity confirm the achievement of the initial objectives.
Стилі APA, Harvard, Vancouver, ISO та ін.
3

Farhadi, Ali, David Forsyth, and Ryan White. "Transfer Learning in Sign language." In 2007 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/cvpr.2007.383346.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
4

Ruder, Sebastian, Matthew E. Peters, Swabha Swayamdipta, and Thomas Wolf. "Transfer Learning in Natural Language Processing." In Proceedings of the 2019 Conference of the North. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/n19-5004.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
5

Stiehm, Sebastian, Larissa Köttgen, Sebastian Thelen, Mario Weisskopf, Florian Welter, Anja Richert, Ingrid Isenhardt, and Sabina Jeschke. "Blended Learning Through Integrating Lego Mindstorms NXT Robots in Engineering Education." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-51641.

Повний текст джерела
Анотація:
The current program for Mechanical Engineering at the RWTH Aachen University in Germany has more than 1500 students enrolled. Lego Mindstorms’ NXT Robots are fully integrated in the current Engineering Education stream to help students practically apply theoretical concepts. The courses Communication and Organizational Development (KOE) and Computer Science in Mechanical Engineering 1 (INFO1), provided by the interdisciplinary institute cluster IMA/ZLW, follow a newly-designed “blended learning” approach. This institute cluster is composed of the Institute of Information Management in Mechanical Engineering (IMA) and the Center for Learning and Knowledge Management (ZLW). These institutes are currently within the Faculty of Mechanical Engineering at RWTH Aachen University. Two years ago, the course KOE was redesigned and redirected towards a “Flipped Classroom” concept by initiating online lectures and a discussion class. Thus, the tutorial class ROBOFLEX as part of the KOE curriculum is introduced. ROBOFLEX is a two-stage business simulation that enables students to experience realistic virtual communication within computer science and engineering disciplines. Students are divided into groups of about thirty people, and become entrepreneurs and founders of start-ups that specialize in the production of innovative robots for the automotive industry. They create these robots using Lego Mindstorms’ NXT. Since its conception, the course INFO1 has been accompanied by a lab component, where students apply the concepts taught in class in a team-focused software design project. In 2011, the lab concept was changed into a two-stage robotics programming project based on Lego Mindstorms’ NXT Robots and the Java programming language. In the first stage, students practice the fundamental programming concepts that are presented in the lecture by completing a series of exercises in a self-paced manner. The second stage focuses on applied problem-solving. In this stage, pairs of students apply the previously-learned programming concepts to program a “pick-and-place” robot that is equipped with various sensors. The integration of Lego Mindstorms’ NXT Robots into these courses also join the concepts of the two described courses. While KOE delivers organizational and communicational skills, INFO1 provides technical and domain-specific skills. Here, the robots represent the connecting element. The problem-based second stage of INFO1 benefits from the skills that are taught in KOE. Because INFO1 is scheduled in the term following the KOE, it offers a direct opportunity for students to transfer the KOE skill set from the lecture where it was taught into a new context that is primarily concerned with a different subject. Both classes have been evaluated and developed independently in the past. Since last year’s introduction of ROBOFLEX in KOE, synergies between both lectures are becoming a main component of their further developments. In this paper the recent developments in both courses will be compared and discussed. Specific measurable effects concerning learning capability, motivation and learning endurance are being portrayed by using blended learning approaches.
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Hintz, Gerold, and Chris Biemann. "Language Transfer Learning for Supervised Lexical Substitution." In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/p16-1012.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
7

Wang, Xu, Chengda Tang, Xiaotian Zhao, Xuancai Li, Zhuolin Jin, Dequan Zheng, and Tiejun Zhao. "Transfer Learning Methods for Spoken Language Understanding." In ICMI '19: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3340555.3356096.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
8

Wang, Dong, and Thomas Fang Zheng. "Transfer learning for speech and language processing." In 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). IEEE, 2015. http://dx.doi.org/10.1109/apsipa.2015.7415532.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
9

Zhu, Su, and Kai Yu. "Concept Transfer Learning for Adaptive Language Understanding." In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/w18-5047.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
10

Shen, Chia-Hao, Janet Y. Sung, and Hung-Yi Lee. "Language Transfer of Audio Word2Vec: Learning Audio Segment Representations Without Target Language Data." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8461305.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.

Звіти організацій з теми "Language transfer (Language learning) Germany"

1

Salter, R., Quyen Dong, Cody Coleman, Maria Seale, Alicia Ruvinsky, LaKenya Walker, and W. Bond. Data Lake Ecosystem Workflow. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40203.

Повний текст джерела
Анотація:
The Engineer Research and Development Center, Information Technology Laboratory’s (ERDC-ITL’s) Big Data Analytics team specializes in the analysis of large-scale datasets with capabilities across four research areas that require vast amounts of data to inform and drive analysis: large-scale data governance, deep learning and machine learning, natural language processing, and automated data labeling. Unfortunately, data transfer between government organizations is a complex and time-consuming process requiring coordination of multiple parties across multiple offices and organizations. Past successes in large-scale data analytics have placed a significant demand on ERDC-ITL researchers, highlighting that few individuals fully understand how to successfully transfer data between government organizations; future project success therefore depends on a small group of individuals to efficiently execute a complicated process. The Big Data Analytics team set out to develop a standardized workflow for the transfer of large-scale datasets to ERDC-ITL, in part to educate peers and future collaborators on the process required to transfer datasets between government organizations. Researchers also aim to increase workflow efficiency while protecting data integrity. This report provides an overview of the created Data Lake Ecosystem Workflow by focusing on the six phases required to efficiently transfer large datasets to supercomputing resources located at ERDC-ITL.
Стилі APA, Harvard, Vancouver, ISO та ін.
Також вас можуть зацікавити розширені добірки на тему "Language transfer (Language learning) Germany" для конкретних типів джерел:
Статті в журналах Дисертації Книги
Ми пропонуємо знижки на всі преміум-плани для авторів, чиї праці увійшли до тематичних добірок літератури. Зв'яжіться з нами, щоб отримати унікальний промокод!

До бібліографії