Thematische Bibliographien / Word-for-word translations

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Auswahl der wissenschaftlichen Literatur zum Thema „Word-for-word translations“

Autor: Grafiati
Veröffentlicht am 25. Juni 2021
Zuletzt aktualisiert am 6. Februar 2022

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Zeitschriftenartikel zum Thema "Word-for-word translations"

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Urazayeva, Kuralay B. „Kazakh Translations of M. Lermontov: “Alien” Text and Word-for-Word Translation“. Journal of Siberian Federal University. Humanities & Social Sciences 9, Nr. 5 (Mai 2016): 1210–20. http://dx.doi.org/10.17516/1997-1370-2016-9-5-1210-1220.

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Hussein El-Omari, Abdallah. „Lexical Meaning Translation of the Root Word in the Holy Qur’an; the Word “KATABA” an Example“. World Journal of Educational Research 7, Nr. 4 (19.10.2020): p30. http://dx.doi.org/10.22158/wjer.v7n4p30.

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This study aims at investigating the translation of the lexical meaning of the root word verb in the Holy Quran. A relatively old and a contemporary translations of the meaning of the Holy Quran are employed to investigate the meaning of the word verb “KATABA”. These are George Sale’s and King Fahd Complex for the Printing of the Holy Quran’s translations of its meaning. The word appears eight times in six chapters (“suras” in Arabic) in the Holy Quran. The two translations give six synonyms for the word through the eight different contexts. The study finds that understanding the context, environment, and reason of revelation guide the translator find the proper equivalent of the Holy Quran word. It is also found that translating the Holy Quran with its miraculous and high linguistic style is not easy. Similarly, translating a word isolated out of its context will turn into a literal distorted text. Furthermore, it is also found that using footnotes and explanations is inevitable.
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Zhang, Chun Xiang, Long Deng, Xue Yao Gao und Li Li Guo. „Word Sense Disambiguation for Improving the Quality of Machine Translation“. Advanced Materials Research 981 (Juli 2014): 153–56. http://dx.doi.org/10.4028/www.scientific.net/amr.981.153.

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Word sense disambiguation is key to many application problems in natural language processing. In this paper, a specific classifier of word sense disambiguation is introduced into machine translation system in order to improve the quality of the output translation. Firstly, translation of ambiguous word is deleted from machine translation of Chinese sentence. Secondly, ambiguous word is disambiguated and the classification labels are translations of ambiguous word. Thirdly, these two translations are combined. 50 Chinese sentences including ambiguous words are collected for test experiments. Experimental results show that the translation quality is improved after the proposed method is applied.
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Leonavičienė, Aurelija. „Interpretation and Translation of Intertextual Meanings of Lithuanian Literature into French“. Respectus Philologicus 23, Nr. 28 (25.04.2013): 97–108. http://dx.doi.org/10.15388/respectus.2013.23.28.8.

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This article analyses the intertextual meanings of Lithuanian literature, how they are interpreted, and some tendencies of their translation into French. The material for the analysis comprises 27 Lithuanian literature novels and ten poems, together with their translations into French (published from 2000–2010). The analysis shows the tendencies of translation of intertextual meanings during the last decade. The results of the quantitative research indicate that intertextual meanings are mainly translated by proper names, meaningful word groups, and phrases. A dominant tendency when translating intertextual meanings into French is translation without changes, when the intertextual meaning is understood equivalently in both the source and target cultures, without the need for additional explanation. Other translation strategies (explicit rendering of intertextual meaning; wordfor-word translation or “internal emphasis”) were applied more rarely. Even though the examples of word-for-word translation comprise only one-fifth of all analysed intertextual meanings, the results of their analysis suggest that translators sometimes fail to choose appropriate translation strategies and translate the word forms of the intertextual units; in such cases, the translations lose important intertextual connections, intellectual and emotional connotations are neutralized, and the readers of the translation face “culture bumps.”
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DEGANI, TAMAR, und NATASHA TOKOWICZ. „Ambiguous words are harder to learn“. Bilingualism: Language and Cognition 13, Nr. 3 (19.01.2010): 299–314. http://dx.doi.org/10.1017/s1366728909990411.

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Relatively little is known about the role of ambiguity in adult second-language learning. In this study, native English speakers learned Dutch–English translation pairs that either mapped in a one-to-one fashion (unambiguous items) in that a Dutch word uniquely corresponded to one English word, or mapped in a one-to-many fashion (ambiguous items), with two Dutch translations corresponding to a single English word. These two Dutch translations could function as exact synonyms, corresponding to a single meaning, or could correspond to different meanings of an ambiguous English word (e.g., wisselgeld denotes the monetary meaning of the word change, and verandering denotes alteration). Several immediate and delayed tests revealed that such translation ambiguity creates a challenge for learners. Furthermore, words with multiple translations corresponding to the same meaning are more difficult to learn than words with multiple translations corresponding to multiple meanings, suggesting that a one-to-many mapping underlies this ambiguity disadvantage.
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Ueffing, Nicola, und Hermann Ney. „Word-Level Confidence Estimation for Machine Translation“. Computational Linguistics 33, Nr. 1 (März 2007): 9–40. http://dx.doi.org/10.1162/coli.2007.33.1.9.

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This article introduces and evaluates several different word-level confidence measures for machine translation. These measures provide a method for labeling each word in an automatically generated translation as correct or incorrect. All approaches to confidence estimation presented here are based on word posterior probabilities. Different concepts of word posterior probabilities as well as different ways of calculating them will be introduced and compared. They can be divided into two categories: System-based methods that explore knowledge provided by the translation system that generated the translations, and direct methods that are independent of the translation system. The system-based techniques make use of system output, such as word graphs or N-best lists. The word posterior probability is determined by summing the probabilities of the sentences in the translation hypothesis space that contains the target word. The direct confidence measures take other knowledge sources, such as word or phrase lexica, into account. They can be applied to output from nonstatistical machine translation systems as well. Experimental assessment of the different confidence measures on various translation tasks and in several language pairs will be presented. Moreover,the application of confidence measures for rescoring of translation hypotheses will be investigated.
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Al-Shalabi, Riyad, Ghassan Kanaan, Huda Al-Sarhan, Alaa Drabsh und Islam Al-Husban. „Evaluating Machine Translations from Arabic into English and Vice Versa“. International Research Journal of Electronics and Computer Engineering 3, Nr. 2 (24.06.2017): 1. http://dx.doi.org/10.24178/irjece.2017.3.2.01.

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Abstract—Machine translation (MT) allows direct communication between two persons without the need for the third party or via dictionary in your pocket, which could bring significant and per formative improvement. Since most traditional translational way is a word-sensitive, it is very important to consider the word order in addition to word selection in the evaluation of any machine translation. To evaluate the MT performance, it is necessary to dynamically observe the translation in the machine translator tool according to word order, and word selection and furthermore the sentence length. However, applying a good evaluation with respect to all previous points is a very challenging issue. In this paper, we first summarize various approaches to evaluate machine translation. We propose a practical solution by selecting an appropriate powerful tool called iBLEU to evaluate the accuracy degree of famous MT tools (i.e. Google, Bing, Systranet and Babylon). Based on the solution structure, we further discuss the performance order for these tools in both directions Arabic to English and English to Arabic. After extensive testing, we can decide that any direction gives more accurate results in translation based on the selected machine translations MTs. Finally, we proved the choosing of Google as best system performance and Systranet as the worst one. Index Terms: Machine Translation, MTs, Evaluation for Machine Translation, Google, Bing, Systranet and Babylon, Machine Translation tools, BLEU, iBLEU.
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Xiao, Richard. „Word clusters and reformulation markers in Chinese and English“. Languages in Contrast 11, Nr. 2 (30.09.2011): 145–71. http://dx.doi.org/10.1075/lic.11.2.01xia.

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This article presents a corpus-based contrastive study of word clusters and reformulation markers in Chinese and English, and discusses the implications of the findings for translation universal hypotheses. The study is based on three balanced comparable corpora which represent British English, native Chinese and translational Chinese in addition to an English-Chinese parallel corpus which provides a basis for comparing native and translated English and investigating explicitation in translation. Our results show that word clusters are substantially more common in translated Chinese, suggesting a tendency in translations to use fixed and semi-fixed recurring patterns in an attempt to achieve improved fluency. The more frequent use of word clusters, especially those of high frequency and high coverage in translational Chinese, is also likely to be a result of the influence of the English source language because word clusters are significantly more prevalent in native English in relation to native Chinese. Chinese and English tend to use reformulation markers of different styles while on the other hand, reformulation markers are generally more common in both translated English and translated Chinese than in their native counterparts, suggesting that reformulation markers function as a strategy for explicitation in translations, which tend to use oral, stylistically simpler forms than non-translated texts.
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Degani, Tamar, Anat Prior, Chelsea M. Eddington, Ana B. Arêas da Luz Fontes und Natasha Tokowicz. „Determinants of translation ambiguity“. Linguistic Approaches to Bilingualism 6, Nr. 3 (25.01.2016): 290–307. http://dx.doi.org/10.1075/lab.14013.deg.

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Abstract Ambiguity in translation is highly prevalent, and has consequences for second-language learning and for bilingual lexical processing. To better understand this phenomenon, the current study compared the determinants of translation ambiguity across four sets of translation norms from English to Spanish, Dutch, German and Hebrew. The number of translations an English word received was correlated across these different languages, and was also correlated with the number of senses the word has in English, demonstrating that translation ambiguity is partially determined by within-language semantic ambiguity. For semantically-ambiguous English words, the probability of the different translations in Spanish and Hebrew was predicted by the meaning-dominance structure in English, beyond the influence of other lexical and semantic factors, for bilinguals translating from their L1, and translating from their L2. These findings are consistent with models postulating direct access to meaning from L2 words for moderately-proficient bilinguals.
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Malcolm, Matthew R. „Governing Imagery and the Translation of the Words philadelphia and anachusis in 1 Peter 1.22 and 4.4“. Bible Translator 70, Nr. 1 (April 2019): 9–15. http://dx.doi.org/10.1177/2051677018823042.

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One element of the task of translation is the need to consider contextual clues in the source text that help to inform choices between alternative possibilities in the target language. I suggest that greater awareness of such contextual clues for two words in 1 Peter would result in better translations than are currently found in two major English Bible versions (NIV2011 and NRSV). Specifically, the context of 1.3–2.3 would suggest that the familial nuance of the word philadelphia should be retained in translation, because this word is part of a section governed by familial imagery. Also, the context of 3.17–4.6 would suggest that the aquatic nuance of the word anachusis should be retained in translation, because this word is part of a section governed by the imagery of the flood. I look into the semantic-pragmatic possibilities for these words, and propose that NRSV and NIV could be improved by changing their translations of these words. Finally, implications for the translations of these words in other English versions and in other languages are discussed.
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Dissertationen zum Thema "Word-for-word translations"

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Kotremagias, Dimitrios. „Das Funktionsverb leisten aus einer Übersetzungsperspektive : Eine kontrastive Studie deutsch-schwedischer Übersetzungen“. Thesis, Linnéuniversitetet, Institutionen för språk (SPR), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105167.

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The aim of this thesis has been to analyze German light verb constructions with the light verb leisten and their translations into Swedish. From the identified types containing a nominalization + leisten as parts of a light verb construction, three construction types were examined more closely, namely Beitrag leisten, Arbeit leisten and Hilfe leisten. These light verb constructions showed a higher representation in the material and were hence selected for a further in-depth study. As for the translations into Swedish, three main translation strategies were identified: word-for-word translations, full-verb replacements, and paraphrases/omissions.  The results show that paraphrasing is the preferred strategy, although the result is quite even between all the strategies. The results also show that each of the three constructions preferred one each of the three translation strategies, which opens for further studies in this area. One explanation for this is, relating to the semantic meaning of the nouns and to their flexible characteristics into Swedish, that nouns with a narrower semantic meaning are more inclined to be translated word-for-word, but also depending on whether the nominalization has a function as a direct object in the sentence, is modified by an adjective, or is merely part of a predicative.
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Åkerström, Johanna. „Translating Song Lyrics : A Study of the Translation of the Three Musicals by Benny Andersson and Björn Ulvaeus“. Thesis, Södertörns högskola, Institutionen för kultur och kommunikation, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-4612.

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The primary aim of the present study is to gain an understanding of the translation process involved when translating song lyrics by investigating to what extent 10 translation features occur in a corpus of 12 song lyrics from the musicals CHESS, MAMMA MIA! and Kristina från Duvemåla. Comparing the source texts to the translated texts, taking into account: number of words, syllables vs. words, word-for-word translations, additions/omissions, metaphors, rhymes, reorganization of text, paraphrases and last if there were any untranslated English words kept in the Swedish version – led to the conclusion that the translation strategy of using paraphrases (express something written in other words) was the most common translation strategy used when translating song lyrics. In addition, translating song lyrics also requires a translator who is musical, has good association skills, a large vocabulary and is also very good at playing with words. Taking the findings into consideration it could be said that the word 'translation' should be avoided in reference to the act of transferring the song lyrics of a musical in one language into another language. More apt descriptive phrases for this process would probably be 'text arrangement' or 'interpretation'.
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Ueffing, Nicola. „Word confidence measures for machine translation“. [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=97967669X.

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Carpuat, Marine Jacinthe. „Word sense disambiguation for statistical machine translation /“. View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?CSED%202008%20CARPUA.

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TOYAMA, Katsuhiko, Kazuhiro IMAI und Yasuhiro OGAWA. „APPLICATION OF WORD ALIGNMENT FOR SUPPORTING ENGLISH TRANSLATION OF JAPANESE STATUTES“. INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2006. http://hdl.handle.net/2237/10410.

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Rudnick, Alexander James. „Cross-Lingual Word Sense Disambiguation for Low-Resource Hybrid Machine Translation“. Thesis, Indiana University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13422906.

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This thesis argues that cross-lingual word sense disambiguation (CL-WSD) can be used to improve lexical selection for machine translation when translating from a resource-rich language into an under-resourced one, especially when relatively little bitext is available. In CL-WSD, we perform word sense disambiguation, considering the senses of a word to be its possible translations into some target language, rather than using a sense inventory developed manually by lexicographers.

Using explicitly trained classifiers that make use of source-language context and of resources for the source language can help machine translation systems make better decisions when selecting target-language words. This is especially the case when the alternative is hand-written lexical selection rules developed by researchers with linguistic knowledge of the source and target languages, but also true when lexical selection would be performed by a statistical machine translation system, when there is a relatively small amount of available target-language text for training language models.

In this work, I present the Chipa system for CL-WSD and apply it to the task of translating from Spanish to Guarani and Quechua, two indigenous languages of South America. I demonstrate several extensions to the basic Chipa system, including techniques that allow us to benefit from the wealth of available unannotated Spanish text and existing text analysis tools for Spanish, as well as approaches for learning from bitext resources that pair Spanish with languages unrelated to our intended target languages. Finally, I provide proof-of-concept integrations of Chipa with existing machine translation systems, of two completely different architectures.

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Goto, Isao. „Word Reordering for Statistical Machine Translation via Modeling Structural Differences between Languages“. 京都大学 (Kyoto University), 2014. http://hdl.handle.net/2433/189374.

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2015-05-27に本文を差替
Kyoto University (京都大学)
0048
新制・課程博士
博士(情報学)
甲第18481号
情博第532号
新制||情||94(附属図書館)
31359
京都大学大学院情報学研究科知能情報学専攻
(主査)教授 黒橋 禎夫, 教授 田中 克己, 教授 河原 達也
学位規則第4条第1項該当
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Esplà-Gomis, Miquel. „Using external sources of bilingual information for word-level quality estimation in translation technologies“. Doctoral thesis, Universidad de Alicante, 2016. http://hdl.handle.net/10045/54710.

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Tillmann, Christoph [Verfasser], und Hermann [Akademischer Betreuer] Ney. „Word re-ordering and dynamic programming based search algorithm for statistical machine translation / Christoph Tillmann ; Betreuer: Hermann Ney“. Aachen : Universitätsbibliothek der RWTH Aachen, 2001. http://d-nb.info/1129260615/34.

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Ngo, Ho Anh Khoa. „Generative Probabilistic Alignment Models for Words and Subwords : a Systematic Exploration of the Limits and Potentials of Neural Parametrizations“. Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG014.

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L'alignement consiste à mettre en correspondance des unités au sein de bitextes, associant un texte en langue source et sa traduction dans une langue cible. L'alignement peut se concevoir à plusieurs niveaux: entre phrases, entre groupes de mots, entre mots, voire à un niveau plus fin lorsque l'une des langues est morphologiquement complexe, ce qui implique d'aligner des fragments de mot (morphèmes). L'alignement peut être envisagé également sur des structures linguistiques plus complexes des arbres ou des graphes. Il s'agit d'une tâche complexe, sous-spécifiée, que les humains réalisent avec difficulté. Son automatisation est un problème exemplaire du traitement des langues, historiquement associé aux premiers modèles de traduction probabilistes. L'arrivée à maturité de nouveaux modèles pour le traitement automatique des langues, reposant sur des représentationts distribuées calculées par des réseaux de neurones permet de reposer la question du calcul de ces alignements. Cette recherche vise donc à concevoir des modèles neuronaux susceptibles d'être appris sans supervision pour dépasser certaines des limitations des modèles d'alignement statistique et améliorer l'état de l'art en matière de précision des alignements automatiques
Alignment consists of establishing a mapping between units in a bitext, combining a text in a source language and its translation in a target language. Alignments can be computed at several levels: between documents, between sentences, between phrases, between words, or even between smaller units end when one of the languages is morphologically complex, which implies to align fragments of words (morphemes). Alignments can also be considered between more complex linguistic structures such as trees or graphs. This is a complex, under-specified task that humans accomplish with difficulty. Its automation is a notoriously difficult problem in natural language processing, historically associated with the first probabilistic word-based translation models. The design of new models for natural language processing, based on distributed representations computed by neural networks, allows us to question and revisit the computation of these alignments. This research project, therefore, aims to comprehensively understand the limitations of existing statistical alignment models and to design neural models that can be learned without supervision to overcome these drawbacks and to improve the state of art in terms of alignment accuracy
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Bücher zum Thema "Word-for-word translations"

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Schubert, Franz. Schubert's complete song texts: With international phonetic alphabet transcriptions, word for word translations and commentary. Geneseo, N.Y. (Box 384, Geneseo 14454): Leyerle Publications, 1996.

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Stenhammar, Wilhelm. Thirty songs of Wilhelm Stenhammar: With International Phonetic Alphabet transcriptions, word-for-word translations and commentary. Geneseo, N.Y: Leyerle Publications, 1999.

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They have a word for it: A lighthearted lexicon of untranslatable words and phrases. Los Angeles: J.P. Tarcher, 1988.

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A mechanical translation of the Book of Exodus: The Hebrew text literally translated word for word. College Station, TX: Virtualbookworm.com Pub., 2009.

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Moustafa, Elshafei, Hrsg. Cross-word modeling for Arabic speech recognition. New York, NY: Springer, 2012.

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Irma, Schotsman, und Kendrīya-Tibbatī-Ucca-Śikṣā-Saṃsthānam, Hrsg. Aśvaghoṣa's Buddhacarita: The life of Buddha : Sanskrit text with word-by-word translation, melodies for chanting and verses in English grammatical explanation. Saranath, Varanasi: Central Institute of Higher Tibetan Studies, 1995.

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Swami, Chinmayananda. Discourses on Muṇḍakopaniṣad: Original Upaniṣad text in Devanāgarī with transliteration in roman letters, word-for-word meaning in text order with translation and commentary. Mumbai: Central Chinmaya Mission Trust, 2012.

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Discourses on Aṣṭāvakra Gītā: Original Upaniṣad text in Devanāgrī with transliteration in roman letters, word-for-word meaning in text order with translation and commentary. Mumbai: Central Chinmaya Mission Trust, 1997.

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Swami, Chinmayananda. Discourses on Māṇḍukya Upaniṣad with Gauḍapāda's Kārikā: Original Upaniṣad text in Devanāgarī with transliteration in roman letters, word-for-word meaning in text order with translation and commentary. Mumbai: Central Chinmaya Mission Trust, 2011.

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Sankaracarya. Saundarya lahari of Śaṅkarācārya: Sanskrit text with English verse wise word to word translation and transliteration with the commentary of Lakshmi Dhara Sastry with Yantras for the individual hundred slokas with bijaksharas. Hyderabad: Sākhyāyana Vidyā Parishat, 1999.

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Buchteile zum Thema "Word-for-word translations"

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Abutalipov, Alikhan, Aigerim Janaliyeva, Medet Mukushev, Antonio Cerone und Anara Sandygulova. „Handshape Classification in a Reverse Dictionary of Sign Languages for the Deaf“. In From Data to Models and Back, 217–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70650-0_14.

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AbstractThis paper showcases the work that aims at building a user-friendly mobile application of a reverse dictionary to translate sign languages to spoken languages. The concept behind the reverse dictionary is the ability to perform a video-based search by demonstrating a handshape in front of a mobile phone’s camera. The user would be able to use this feature in two ways. Firstly, the user would be able to search for a word by showing a handshape for the application to provide a list of signs that contain that handshape. Secondly, the user could fingerspell the word letter by letter in front of the camera for the application to return the sign that corresponds to that word. The user can then look through the suggested videos and see their written translations. To offer other functionalities, the application also has Search by Category and Search by Word options. Currently, the reverse dictionary supports translations from Russian Sign Language (RSL) to Russian language.
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Paul, Michael, Andrew Finch und Eiichiro Sumita. „Word Segmentation for Dialect Translation“. In Computational Linguistics and Intelligent Text Processing, 55–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19437-5_5.

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Casteleiro, João, Gabriel Pereira Lopes und Joaquim Silva. „Bilingually Learning Word Senses for Translation“. In Computational Linguistics and Intelligent Text Processing, 283–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54903-8_24.

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He, Qiuxiang, Guoping Huang, Lemao Liu und Li Li. „Word Position Aware Translation Memory for Neural Machine Translation“. In Natural Language Processing and Chinese Computing, 367–79. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32233-5_29.

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Li, Qiang, Dongdong Zhang, Mu Li, Tong Xiao und Jingbo Zhu. „Better Addressing Word Deletion for Statistical Machine Translation“. In Natural Language Understanding and Intelligent Applications, 91–102. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50496-4_8.

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Dagan, I., K. Church und W. Gale. „Robust Bilingual Word Alignment for Machine Aided Translation“. In Text, Speech and Language Technology, 209–24. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-017-2390-9_13.

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Junczys-Dowmunt, Marcin, und Arkadiusz Szał. „SyMGiza++: Symmetrized Word Alignment Models for Statistical Machine Translation“. In Security and Intelligent Information Systems, 379–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25261-7_30.

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Li, Qiang, Yaqian Han, Tong Xiao und Jingbo Zhu. „Context Sensitive Word Deletion Model for Statistical Machine Translation“. In Lecture Notes in Computer Science, 73–84. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69005-6_7.

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Yu, Hosang, Gil-Jin Jang und Minho Lee. „Hybridized Character-Word Embedding for Korean Traditional Document Translation“. In Neural Information Processing, 82–89. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04182-3_8.

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Kartbayev, Amandyk. „Learning Word Alignment Models for Kazakh-English Machine Translation“. In Lecture Notes in Computer Science, 326–35. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25135-6_31.

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Konferenzberichte zum Thema "Word-for-word translations"

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Meng, Fandong, Zhaopeng Tu, Yong Cheng, Haiyang Wu, Junjie Zhai, Yuekui Yang und Di Wang. „Neural Machine Translation with Key-Value Memory-Augmented Attention“. In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/357.

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Although attention-based Neural Machine Translation (NMT) has achieved remarkable progress in recent years, it still suffers from issues of repeating and dropping translations. To alleviate these issues, we propose a novel key-value memory-augmented attention model for NMT, called KVMEMATT. Specifically, we maintain a timely updated keymemory to keep track of attention history and a fixed value-memory to store the representation of source sentence throughout the whole translation process. Via nontrivial transformations and iterative interactions between the two memories, the decoder focuses on more appropriate source word(s) for predicting the next target word at each decoding step, therefore can improve the adequacy of translations. Experimental results on Chinese)English and WMT17 German,English translation tasks demonstrate the superiority of the proposed model.
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Chen, Shizhe, Qin Jin und Jianlong Fu. „From Words to Sentences: A Progressive Learning Approach for Zero-resource Machine Translation with Visual Pivots“. In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/685.

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The neural machine translation model has suffered from the lack of large-scale parallel corpora. In contrast, we humans can learn multi-lingual translations even without parallel texts by referring our languages to the external world. To mimic such human learning behavior, we employ images as pivots to enable zero-resource translation learning. However, a picture tells a thousand words, which makes multi-lingual sentences pivoted by the same image noisy as mutual translations and thus hinders the translation model learning. In this work, we propose a progressive learning approach for image-pivoted zero-resource machine translation. Since words are less diverse when grounded in the image, we first learn word-level translation with image pivots, and then progress to learn the sentence-level translation by utilizing the learned word translation to suppress noises in image-pivoted multi-lingual sentences. Experimental results on two widely used image-pivot translation datasets, IAPR-TC12 and Multi30k, show that the proposed approach significantly outperforms other state-of-the-art methods.
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Shmelev, A. D. „LANGUAGE-SPECIFIC WORDS IN THE LIGHT OF TRANSLATION: THE RUSSIAN TOSKA“. In International Conference on Computational Linguistics and Intellectual Technologies "Dialogue". Russian State University for the Humanities, 2020. http://dx.doi.org/10.28995/2075-7182-2020-19-658-669.

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This paper presents a semantic analysis of the most language-specific Russian word for ‘sadness’, namely, toska. The analysis is based on the hypothesis that one may regard translation equivalents and paraphrases of a linguistic unit extracted from real translated texts as a source of information about its semantics. The appearance of language-specific words in translated texts may be even more useful for studying their semantics. It turns out that тоска is not all that rare in Russian translated texts. The study of the incentives that lead Russian translators to use the word тоска often reveals important aspects of the semantics of this word. Stimuli for the appearance of toska in translations into Russian vary greatly. In general, when the original describes some bad feelings, the word toska appears if the original speaks of a subject’s unsatisfied desire, which desire may be vague and not well understood an
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Zhao, Yang, Yining Wang, Jiajun Zhang und Chengqing Zong. „Phrase Table as Recommendation Memory for Neural Machine Translation“. In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/641.

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Neural Machine Translation (NMT) has drawn much attention due to its promising translation performance recently. However, several studies indicate that NMT often generates fluent but unfaithful translations. In this paper, we propose a method to alleviate this problem by using a phrase table as recommendation memory. The main idea is to add bonus to words worthy of recommendation, so that NMT can make correct predictions. Specifically, we first derive a prefix tree to accommodate all the candidate target phrases by searching the phrase translation table according to the source sentence.Then, we construct a recommendation word set by matching between candidate target phrases and previously translated target words by NMT. After that, we determine the specific bonus value for each recommendable word by using the attention vector and phrase translation probability. Finally,we integrate this bonus value into NMT to improve the translation results. The extensive experiments demonstrate that the proposed methods obtain remarkable improvements over the strong attention based NMT.
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Xu, Hongfei, Josef van Genabith, Qiuhui Liu und Deyi Xiong. „Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers“. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.naacl-main.7.

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Isozaki, Hideki, Natsume Kouchi und Tsutomu Hirao. „Dependency-based Automatic Enumeration of Semantically Equivalent Word Orders for Evaluating Japanese Translations“. In Proceedings of the Ninth Workshop on Statistical Machine Translation. Stroudsburg, PA, USA: Association for Computational Linguistics, 2014. http://dx.doi.org/10.3115/v1/w14-3335.

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Xing, Chao, Dong Wang, Chao Liu und Yiye Lin. „Normalized Word Embedding and Orthogonal Transform for Bilingual Word Translation“. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA, USA: Association for Computational Linguistics, 2015. http://dx.doi.org/10.3115/v1/n15-1104.

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Zens, Richard, und Hermann Ney. „Word graphs for statistical machine translation“. In the ACL Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2005. http://dx.doi.org/10.3115/1654449.1654491.

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Schroeder, Josh, Trevor Cohn und Philipp Koehn. „Word lattices for multi-source translation“. In the 12th Conference of the European Chapter of the Association for Computational Linguistics. Morristown, NJ, USA: Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1609067.1609147.

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Vickrey, David, Luke Biewald, Marc Teyssier und Daphne Koller. „Word-sense disambiguation for machine translation“. In the conference. Morristown, NJ, USA: Association for Computational Linguistics, 2005. http://dx.doi.org/10.3115/1220575.1220672.

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Berichte der Organisationen zum Thema "Word-for-word translations"

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Diab, Mona, und Steve Finch. A Statistical Word-Level Translation Model for Comparable Corpora. Fort Belvoir, VA: Defense Technical Information Center, Juni 2000. http://dx.doi.org/10.21236/ada455144.

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Yatsymirska, Mariya. KEY IMPRESSIONS OF 2020 IN JOURNALISTIC TEXTS. Ivan Franko National University of Lviv, März 2021. http://dx.doi.org/10.30970/vjo.2021.50.11107.

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The article explores the key vocabulary of 2020 in the network space of Ukraine. Texts of journalistic, official-business style, analytical publications of well-known journalists on current topics are analyzed. Extralinguistic factors of new word formation, their adaptation to the sphere of special and socio-political vocabulary of the Ukrainian language are determined. Examples show modern impressions in the media, their stylistic use and impact on public opinion in a pandemic. New meanings of foreign expressions, media terminology, peculiarities of translation of neologisms from English into Ukrainian have been clarified. According to the materials of the online media, a «dictionary of the coronavirus era» is provided. The journalistic text functions in the media on the basis of logical judgments, credible arguments, impressive language. Its purpose is to show the socio-political problem, to sharpen its significance for society and to propose solutions through convincing considerations. Most researchers emphasize the influential role of journalistic style, which through the media shapes public opinion on issues of politics, economics, education, health care, war, the future of the country. To cover such a wide range of topics, socio-political vocabulary is used first of all – neutral and emotionally-evaluative, rhetorical questions and imperatives, special terminology, foreign words. There is an ongoing discussion in online publications about the use of the new foreign token «lockdown» instead of the word «quarantine», which has long been learned in the Ukrainian language. Research on this topic has shown that at the initial stage of the pandemic, the word «lockdown» prevailed in the colloquial language of politicians, media personalities and part of society did not quite understand its meaning. Lockdown, in its current interpretation, is a restrictive measure to protect people from a dangerous virus that has spread to many countries; isolation of the population («stay in place») in case of risk of spreading Covid-19. In English, US citizens are told what a lockdown is: «A lockdown is a restriction policy for people or communities to stay where they are, usually due to specific risks to themselves or to others if they can move and interact freely. The term «stay-at-home» or «shelter-in-place» is often used for lockdowns that affect an area, rather than specific locations». Content analysis of online texts leads to the conclusion that in 2020 a special vocabulary was actively functioning, with the appropriate definitions, which the media described as a «dictionary of coronavirus vocabulary». Media broadcasting is the deepest and pulsating source of creative texts with new meanings, phrases, expressiveness. The influential power of the word finds its unconditional embodiment in the media. Journalists, bloggers, experts, politicians, analyzing current events, produce concepts of a new reality. The world is changing and the language of the media is responding to these changes. It manifests itself most vividly and emotionally in the network sphere, in various genres and styles.
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