Relevant bibliographies by topics / Cross-Lingual Mapping

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Cross-Lingual Mapping'

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Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Cross-Lingual Mapping"

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Fu, Zuohui, Yikun Xian, Shijie Geng, Yingqiang Ge, Yuting Wang, Xin Dong, Guang Wang, and Gerard De Melo. "ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7756–63. http://dx.doi.org/10.1609/aaai.v34i05.6279.

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A number of cross-lingual transfer learning approaches based on neural networks have been proposed for the case when large amounts of parallel text are at our disposal. However, in many real-world settings, the size of parallel annotated training data is restricted. Additionally, prior cross-lingual mapping research has mainly focused on the word level. This raises the question of whether such techniques can also be applied to effortlessly obtain cross-lingually aligned sentence representations. To this end, we propose an Adversarial Bi-directional Sentence Embedding Mapping (ABSent) framework, which learns mappings of cross-lingual sentence representations from limited quantities of parallel data. The experiments show that our method outperforms several technically more powerful approaches, especially under challenging low-resource circumstances. The source code is available from https://github.com/zuohuif/ABSent along with relevant datasets.
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Gao, Jiahui, Yi Zhou, Philip L. H. Yu, Shafiq Joty, and Jiuxiang Gu. "UNISON: Unpaired Cross-Lingual Image Captioning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 10654–62. http://dx.doi.org/10.1609/aaai.v36i10.21310.

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Image captioning has emerged as an interesting research field in recent years due to its broad application scenarios. The traditional paradigm of image captioning relies on paired image-caption datasets to train the model in a supervised manner. However, creating such paired datasets for every target language is prohibitively expensive, which hinders the extensibility of captioning technology and deprives a large part of the world population of its benefit. In this work, we present a novel unpaired cross-lingual method to generate image captions without relying on any caption corpus in the source or the target language. Specifically, our method consists of two phases: (1) a cross-lingual auto-encoding process, which utilizing a sentence parallel (bitext) corpus to learn the mapping from the source to the target language in the scene graph encoding space and decode sentences in the target language, and (2) a cross-modal unsupervised feature mapping, which seeks to map the encoded scene graph features from image modality to language modality. We verify the effectiveness of our proposed method on the Chinese image caption generation task. The comparisons against several existing methods demonstrate the effectiveness of our approach.
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Li, Juntao, Chang Liu, Jian Wang, Lidong Bing, Hongsong Li, Xiaozhong Liu, Dongyan Zhao, and Rui Yan. "Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 8212–19. http://dx.doi.org/10.1609/aaai.v34i05.6335.

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With the prosperous of cross-border e-commerce, there is an urgent demand for designing intelligent approaches for assisting e-commerce sellers to offer local products for consumers from all over the world. In this paper, we explore a new task of cross-lingual information retrieval, i.e., cross-lingual set-to-description retrieval in cross-border e-commerce, which involves matching product attribute sets in the source language with persuasive product descriptions in the target language. We manually collect a new and high-quality paired dataset, where each pair contains an unordered product attribute set in the source language and an informative product description in the target language. As the dataset construction process is both time-consuming and costly, the new dataset only comprises of 13.5k pairs, which is a low-resource setting and can be viewed as a challenging testbed for model development and evaluation in cross-border e-commerce. To tackle this cross-lingual set-to-description retrieval task, we propose a novel cross-lingual matching network (CLMN) with the enhancement of context-dependent cross-lingual mapping upon the pre-trained monolingual BERT representations. Experimental results indicate that our proposed CLMN yields impressive results on the challenging task and the context-dependent cross-lingual mapping on BERT yields noticeable improvement over the pre-trained multi-lingual BERT model.
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Abu Helou, Mamoun, Matteo Palmonari, and Mustafa Jarrar. "Effectiveness of Automatic Translations for Cross-Lingual Ontology Mapping." Journal of Artificial Intelligence Research 55 (January 25, 2016): 165–208. http://dx.doi.org/10.1613/jair.4789.

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Accessing or integrating data lexicalized in different languages is a challenge. Multilingual lexical resources play a fundamental role in reducing the language barriers to map concepts lexicalized in different languages. In this paper we present a large-scale study on the effectiveness of automatic translations to support two key cross-lingual ontology mapping tasks: the retrieval of candidate matches and the selection of the correct matches for inclusion in the final alignment. We conduct our experiments using four different large gold standards, each one consisting of a pair of mapped wordnets, to cover four different families of languages. We categorize concepts based on their lexicalization (type of words, synonym richness, position in a subconcept graph) and analyze their distributions in the gold standards. Leveraging this categorization, we measure several aspects of translation effectiveness, such as word-translation correctness, word sense coverage, synset and synonym coverage. Finally, we thoroughly discuss several findings of our study, which we believe are helpful for the design of more sophisticated cross-lingual mapping algorithms.
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Song, Yuting, Biligsaikhan Batjargal, and Akira Maeda. "Learning Japanese-English Bilingual Word Embeddings by Using Language Specificity." International Journal of Asian Language Processing 30, no. 03 (September 2020): 2050014. http://dx.doi.org/10.1142/s2717554520500149.

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Cross-lingual word embeddings have been gaining attention because they can capture the semantic meaning of words across languages, which can be applied to cross-lingual tasks. Most methods learn a single mapping (e.g., a linear mapping) to transform a word embedding space from one language to another. To improve bilingual word embeddings, we propose an advanced method that adds a language-specific mapping. We focus on learning Japanese-English bilingual word embedding mapping by considering the specificity of the Japanese language. We evaluated our method by comparing it with single mapping-based-models on bilingual lexicon induction between Japanese and English. We determined that our method was more effective, with significant improvements on words of Japanese origin.
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Fu, Bo, Rob Brennan, and Declan O’Sullivan. "A configurable translation-based cross-lingual ontology mapping system to adjust mapping outcomes." Journal of Web Semantics 15 (September 2012): 15–36. http://dx.doi.org/10.1016/j.websem.2012.06.001.

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Robnik-Šikonja, Marko, Kristjan Reba, and Igor Mozetič. "Cross-lingual transfer of sentiment classifiers." Slovenščina 2.0: empirical, applied and interdisciplinary research 9, no. 1 (July 6, 2021): 1–25. http://dx.doi.org/10.4312/slo2.0.2021.1.1-25.

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Word embeddings represent words in a numeric space so that semantic relations between words are represented as distances and directions in the vector space. Cross-lingual word embeddings transform vector spaces of different languages so that similar words are aligned. This is done by mapping one language’s vector space to the vector space of another language or by construction of a joint vector space for multiple languages. Cross-lingual embeddings can be used to transfer machine learning models between languages, thereby compensating for insufficient data in less-resourced languages. We use cross-lingual word embeddings to transfer machine learning prediction models for Twitter sentiment between 13 languages. We focus on two transfer mechanisms that recently show superior transfer performance. The first mechanism uses the trained models whose input is the joint numerical space for many languages as implemented in the LASER library. The second mechanism uses large pretrained multilingual BERT language models. Our experiments show that the transfer of models between similar languages is sensible, even with no target language data. The performance of cross-lingual models obtained with the multilingual BERT and LASER library is comparable, and the differences are language-dependent. The transfer with CroSloEngual BERT, pretrained on only three languages, is superior on these and some closely related languages.
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Bhowmik, Kowshik, and Anca Ralescu. "Clustering of Monolingual Embedding Spaces." Digital 3, no. 1 (February 23, 2023): 48–66. http://dx.doi.org/10.3390/digital3010004.

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Suboptimal performance of cross-lingual word embeddings for distant and low-resource languages calls into question the isomorphic assumption integral to the mapping-based methods of obtaining such embeddings. This paper investigates the comparative impact of typological relationship and corpus size on the isomorphism between monolingual embedding spaces. To that end, two clustering algorithms were applied to three sets of pairwise degrees of isomorphisms. It is also the goal of the paper to determine the combination of the isomorphism measure and clustering algorithm that best captures the typological relationship among the chosen set of languages. Of the three measures investigated, Relational Similarity seemed to capture best the typological information of the languages encoded in their respective embedding spaces. These language clusters can help us identify, without any pre-existing knowledge about the real-world linguistic relationships shared among a group of languages, the related higher-resource languages of low-resource languages. The presence of such languages in the cross-lingual embedding space can help improve the performance of low-resource languages in a cross-lingual embedding space.
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DO, Van Hai, Xiong XIAO, Eng Siong CHNG, and Haizhou LI. "Cross-Lingual Phone Mapping for Large Vocabulary Speech Recognition of Under-Resourced Languages." IEICE Transactions on Information and Systems E97.D, no. 2 (2014): 285–95. http://dx.doi.org/10.1587/transinf.e97.d.285.

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Shi, Xiayang, Ping Yue, Xinyi Liu, Chun Xu, and Lin Xu. "Obtaining Parallel Sentences in Low-Resource Language Pairs with Minimal Supervision." Computational Intelligence and Neuroscience 2022 (August 3, 2022): 1–9. http://dx.doi.org/10.1155/2022/5296946.

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Machine translation relies on parallel sentences, the number of which is an important factor affecting the performance of machine translation systems, especially in low-resource languages. Recent advances in learning cross-lingual word representations from nonparallel data by machine learning make a new possibility for obtaining bilingual sentences with minimal supervision in low-resource languages. In this paper, we introduce a novel methodology to obtain parallel sentences via only a small-size bilingual seed lexicon about hundreds of entries. We first obtain bilingual semantic by establishing cross-lingual mapping in monolingual languages via a seed lexicon. Then, we construct a deep learning classifier to extract bilingual parallel sentences. We demonstrate the effectiveness of our methodology by harvesting Uyghur-Chinese parallel sentences and constructing a machine translation system. The experiments indicate that our method can obtain large and high-accuracy bilingual parallel sentences in low-resource language pairs.
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Dissertations / Theses on the topic "Cross-Lingual Mapping"

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ABU, HELOU MAMOUN. "Cross-Lingual Mapping of Lexical Ontologies with Automatic Translation." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2016. http://hdl.handle.net/10281/102411.

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Il Web offre una quantità sempre più grande di dati multilingua disponibili in un numero elevato di sorgenti informative. Le ontologie sono state proposte per facilitare lo scambio e l'integrazione di dati tra più applicazioni diverse. Al fine di integrare sorgenti informative che utilizzano ontologie diverse è necessario stabilire delle corrispondenze (i.e., mappings) tra concetti ontologici specificati in tali ontologie. Il processo di generazione di tali corrispondenze tra concetti lessicalizzati in lingue diverse prende il nome di cross-lingual ontology mapping. Il cross-lingual ontology mapping ed è considerato attualmente una sfida difficile e gioca un ruolo fondamentale nello stabilire relazioni semantiche tra concetti lessicalizzati in lingue differenti al fine, ad esempio, di: allineare due risorse specifiche per linguaggi diversi, creare risorse multi-lingua che possiedano ricche lessicalizzazioni, o supportare l'annotazione di dati bi-lingue. Molti delle tecniche di cross-lingual ontology mapping includono un passo di traduzione automatica in linguaggi diverse delle lessicalizzazioni dei concetti. Uno degli approcci più frequentemente adottati nello stato dell'arte per l'ottenimento di traduzioni automatiche include l'utilizzo di risorse lessicali multi-lingua come ed esempio strumenti di machine translation i quali sono riconosciuti come le fonti più complete attualmente disponibili. Tuttavia, la qualità delle traduzioni ottenute da strumenti di machine translation è limitata ed affetta da rumore; una ragione di questo fenomeno è la natura polisemica e sinonimica del linguaggio naturale. La qualità delle traduzioni utilizzate da un metodo di mapping ne impatta drasticamente l'efficacia. L'obiettivo principale di questa tesi è quello di proporre un metodo automatico per il cross-lingual mapping the sfrutti evidenza lessicale ottenuta da traduzioni automatiche al fine di supportare automaticamente il mapping di concetti in lingue diverse, oppure processi semi-automatici di mapping. In particolare, stabilire mapping tra risorse lessicalmente ricche e molto grandi, come ad esempio le ontologie lessicali. I maggiori contributi di questa tesi possono essere riassunti come segue: propongo una interpretazione classification-based dei mapping cross-lingua; analizzo su larga scala l'efficacia delle traduzioni automatiche applicate in processi di cross-lingual mapping; propongo una classificazione dei concetti di ontologie lessicali basata su un insieme di caratteristiche lessicali differenti; propongo un metodo automatico di cross-lingual mapping che utilizza una nuova misura di similarità basata sulle traduzioni ed un algoritmo di ottimizzazione della similarità locale; infine, un'applicazione Web che supporta il mapping semi-automatico basato sul metodo proposto
In the Web, multilingual data are growing fast and exist in a large number of sources. \emph{Ontologies} have been proposed for the ease of data exchange and integration across applications. When data sources using different ontologies have to be integrated, mappings between the concepts described in these ontologies have to be established. \emph{Cross-lingual ontology mapping} is the task of establishing mappings between concepts lexicalized in different languages. Cross-lingual ontology mapping is currently considered an important challenge, which plays a fundamental role in establishing semantic relations between concepts lexicalized in different languages, in order to align two language-based resources; to create multilingual lexical resources with rich lexicalizations; or to support a bilingual data annotation. Most of the cross-lingual mapping methods include a step in which the concepts' lexicalizations are automatically translated into different languages. One of the most frequently adopted approaches in the state-of-the-art to obtain automatic translations includes the use of \textit{multilingual lexical resources}, such as machine translation tools, which have been recognized as the largest available resources for translations. However, translation quality achieved by machine translation is limited and affected by noise; one reason of this quality is due to the polysemous and synonymous nature of natural languages. The quality of the translations used by a mapping method has a major impact on its performance. The main goal of this thesis is to provide an automatic cross-lingual mapping method that leverages lexical evidence obtained from automatic translations, in order to automatically support the decision in mapping concepts across different languages, or even to support semi-automatic mapping workflows. In particular, in establishing mappings between very large, lexically-rich resources, e.g., lexical ontologies. The major contributions of this thesis can be summarized as follows: I presents a classification-based interpretation for cross-lingual mappings; I analyze at a large-scale the effectiveness of automatic translations on cross-lingual mapping tasks; I classifies concepts in lexical ontologies based on different lexical characteristics; I proposes an automatic cross-lingual lexical mapping method based on a novel translation-based similarity measure and a local similarity optimization algorithm; finally, I implements a Web tool that supports a semi-automatic mapping approach based on the proposed method.
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Landegren, Nils. "How Sensitive Are Cross-Lingual Mappings to Data-Specific Factors?" Thesis, Stockholms universitet, Institutionen för lingvistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-185069.

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Lesnikova, Tatiana. "Liage de données RDF : évaluation d'approches interlingues." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAM011/document.

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Le Web des données étend le Web en publiant des données structurées et liées en RDF. Un jeu de données RDF est un graphe orienté où les ressources peuvent être des sommets étiquetées dans des langues naturelles. Un des principaux défis est de découvrir les liens entre jeux de données RDF. Étant donnés deux jeux de données, cela consiste à trouver les ressources équivalentes et les lier avec des liens owl:sameAs. Ce problème est particulièrement difficile lorsque les ressources sont décrites dans différentes langues naturelles.Cette thèse étudie l'efficacité des ressources linguistiques pour le liage des données exprimées dans différentes langues. Chaque ressource RDF est représentée comme un document virtuel contenant les informations textuelles des sommets voisins. Les étiquettes des sommets voisins constituent le contexte d'une ressource. Une fois que les documents sont créés, ils sont projetés dans un même espace afin d'être comparés. Ceci peut être réalisé à l'aide de la traduction automatique ou de ressources lexicales multilingues. Une fois que les documents sont dans le même espace, des mesures de similarité sont appliquées afin de trouver les ressources identiques. La similarité entre les documents est prise pour la similarité entre les ressources RDF.Nous évaluons expérimentalement différentes méthodes pour lier les données RDF. En particulier, deux stratégies sont explorées: l'application de la traduction automatique et l'usage des banques de données terminologiques et lexicales multilingues. Dans l'ensemble, l'évaluation montre l'efficacité de ce type d'approches. Les méthodes ont été évaluées sur les ressources en anglais, chinois, français, et allemand. Les meilleurs résultats (F-mesure > 0.90) ont été obtenus par la traduction automatique. L'évaluation montre que la méthode basée sur la similarité peut être appliquée avec succès sur les ressources RDF indépendamment de leur type (entités nommées ou concepts de dictionnaires)
The Semantic Web extends the Web by publishing structured and interlinked data using RDF.An RDF data set is a graph where resources are nodes labelled in natural languages. One of the key challenges of linked data is to be able to discover links across RDF data sets. Given two data sets, equivalent resources should be identified and linked by owl:sameAs links. This problem is particularly difficult when resources are described in different natural languages.This thesis investigates the effectiveness of linguistic resources for interlinking RDF data sets. For this purpose, we introduce a general framework in which each RDF resource is represented as a virtual document containing text information of neighboring nodes. The context of a resource are the labels of the neighboring nodes. Once virtual documents are created, they are projected in the same space in order to be compared. This can be achieved by using machine translation or multilingual lexical resources. Once documents are in the same space, similarity measures to find identical resources are applied. Similarity between elements of this space is taken for similarity between RDF resources.We performed evaluation of cross-lingual techniques within the proposed framework. We experimentally evaluate different methods for linking RDF data. In particular, two strategies are explored: applying machine translation or using references to multilingual resources. Overall, evaluation shows the effectiveness of cross-lingual string-based approaches for linking RDF resources expressed in different languages. The methods have been evaluated on resources in English, Chinese, French and German. The best performance (over 0.90 F-measure) was obtained by the machine translation approach. This shows that the similarity-based method can be successfully applied on RDF resources independently of their type (named entities or thesauri concepts). The best experimental results involving just a pair of languages demonstrated the usefulness of such techniques for interlinking RDF resources cross-lingually
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Book chapters on the topic "Cross-Lingual Mapping"

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Ayana, Abraham G., Hailong Cao, and Tiejun Zhao. "Unsupervised Cross-Lingual Mapping for Phrase Embedding Spaces." In Advances in Intelligent Systems and Computing, 512–24. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39442-4_38.

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Fu, Bo, Rob Brennan, and Declan O’Sullivan. "Using Pseudo Feedback to Improve Cross-Lingual Ontology Mapping." In Lecture Notes in Computer Science, 336–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21034-1_23.

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Megawati, Saemi Jang, and Mun Yong Yi. "Utilization of DBpedia Mapping in Cross Lingual Wikipedia Infobox Completion." In AI 2016: Advances in Artificial Intelligence, 303–16. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50127-7_25.

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Abu Helou, Mamoun, and Matteo Palmonari. "Upper Bound for Cross-Lingual Concept Mapping with External Translation Resources." In Natural Language Processing and Information Systems, 424–31. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19581-0_41.

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Fu, Bo, Rob Brennan, and Declan O’Sullivan. "Cross-Lingual Ontology Mapping – An Investigation of the Impact of Machine Translation." In The Semantic Web, 1–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10871-6_1.

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Amaral, Gabriel, Mārcis Pinnis, Inguna Skadiņa, Odinaldo Rodrigues, and Elena Simperl. "Statistical and Neural Methods for Cross-lingual Entity Label Mapping in Knowledge Graphs." In Text, Speech, and Dialogue, 39–51. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16270-1_4.

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Bond, Francis, and Giulia Bonansinga. "Exploring Cross-Lingual Sense Mapping in a Multilingual Parallel Corpus." In Proceedings of the Second Italian Conference on Computational Linguistics CLiC-it 2015, 56–61. Accademia University Press, 2015. http://dx.doi.org/10.4000/books.aaccademia.1321.

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Conference papers on the topic "Cross-Lingual Mapping"

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Aldarmaki, Hanan, and Mona Diab. "Context-Aware Cross-Lingual Mapping." In Proceedings of the 2019 Conference of the North. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/n19-1391.

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Ivanova, Tatyana. "Cross-lingual and multilingual ontology mapping - survey." In CompSysTech'18: 19th International Conference on Computer Systems and Technologies. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3274005.3274034.

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Moberg, Marko, Kimmo Parssinen, and Juha Iso-Sipila. "Cross-lingual phoneme mapping for multilingual synthesis systems." In Interspeech 2004. ISCA: ISCA, 2004. http://dx.doi.org/10.21437/interspeech.2004-364.

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Do, Van Hai, Xiong Xiao, Eng Siong Chng, and Haizhou Li. "Context dependant phone mapping for cross-lingual acoustic modeling." In 2012 8th International Symposium on Chinese Spoken Language Processing (ISCSLP 2012). IEEE, 2012. http://dx.doi.org/10.1109/iscslp.2012.6423496.

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Patel, Ami, David Li, Eunjoon Cho, and Petar Aleksic. "Cross-Lingual Phoneme Mapping for Language Robust Contextual Speech Recognition." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8461600.

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Shi, Xiaofei, and Yanghua Xiao. "Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment." In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-1075.

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Cheng, Yi, and Sujian Li. "Zero-shot Chinese Discourse Dependency Parsing via Cross-lingual Mapping." In Proceedings of the 1st Workshop on Discourse Structure in Neural NLG. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-8104.

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Qian, Yao, Ji Xu, and Frank K. Soong. "A frame mapping based HMM approach to cross-lingual voice transformation." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947509.

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Ormazabal, Aitor, Mikel Artetxe, Aitor Soroa, Gorka Labaka, and Eneko Agirre. "Beyond Offline Mapping: Learning Cross-lingual Word Embeddings through Context Anchoring." In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.acl-long.506.

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Sim, Khe Chai, and Haizhou Li. "Stream-based context-sensitive phone mapping for cross-lingual speech recognition." In Interspeech 2009. ISCA: ISCA, 2009. http://dx.doi.org/10.21437/interspeech.2009-764.

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