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Modeling contextual information in neural machine translation
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Distributed representations for multilingual language processing
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Abstract:
Distributed representations are a central element in natural language processing. Units of text such as words, ngrams, or characters are mapped to real-valued vectors so that they can be processed by computational models. Representations trained on large amounts of text, called static word embeddings, have been found to work well across a variety of tasks such as sentiment analysis or named entity recognition. More recently, pretrained language models are used as contextualized representations that have been found to yield even better task performances. Multilingual representations that are invariant with respect to languages are useful for multiple reasons. Models using those representations would only require training data in one language and still generalize across multiple languages. This is especially useful for languages that exhibit data sparsity. Further, machine translation models can benefit from source and target representations in the same space. Last, knowledge extraction models could not only access English data, but data in any natural language and thus exploit a richer source of knowledge. Given that several thousand languages exist in the world, the need for multilingual language processing seems evident. However, it is not immediately clear, which properties multilingual embeddings should exhibit, how current multilingual representations work and how they could be improved. This thesis investigates some of these questions. In the first publication, we explore the boundaries of multilingual representation learning by creating an embedding space across more than one thousand languages. We analyze existing methods and propose concept based embedding learning methods. The second paper investigates differences between creating representations for one thousand languages with little data versus considering few languages with abundant data. In the third publication, we refine a method to obtain interpretable subspaces of embeddings. This method can be used to investigate the workings of multilingual representations. The fourth publication finds that multilingual pretrained language models exhibit a high degree of multilinguality in the sense that high quality word alignments can be easily extracted. The fifth paper investigates reasons why multilingual pretrained language models are multilingual despite lacking any kind of crosslingual supervision during training. Based on our findings we propose a training scheme that leads to improved multilinguality. Last, the sixth paper investigates the use of multilingual pretrained language models as multilingual knowledge bases.
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Keyword:
ddc:000; ddc:004; Fakultät für Mathematik; Informatik und Statistik
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URL: http://nbn-resolving.de/urn:nbn:de:bvb:19-280144
https://edoc.ub.uni-muenchen.de/28014/1/Dufter_Philipp.pdf
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| 5 |
Warum wir so wenig über die Sprachen in Deutschland wissen: Spracheinstellungen als Erkenntnisbarriere
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In: Diskurs Kindheits- und Jugendforschung / Discourse. Journal of Childhood and Adolescence Research ; 16 ; 4 ; 403-419 ; Perspektiven von Kindern und Jugendlichen auf sprachliche Diversität und Sprachbildungsprozesse (2021)
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| 6 |
Konzepte und Guidelines für Applikationen in Cinematic Virtual Reality
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| 7 |
Evaluating Unsupervised Representation Learning for Detecting Stances of Fake News
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| 10 |
Bildungsforschung mit Daten der amtlichen Statistik ; Educational research with data of official statistics
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In: Fickermann, Detlef [Hrsg.]; Weishaupt, Horst [Hrsg.]: Bildungsforschung mit Daten der amtlichen Statistik. Münster ; New York : Waxmann 2019, S. 11-18. - (Die Deutsche Schule, Beiheft; 14) (2019)
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| 11 |
Bildungsforschung mit Daten der amtlichen Statistik
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In: Münster ; New York : Waxmann 2019, 267 S. - (Die Deutsche Schule, Beiheft; 14) (2019)
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| 12 |
Multilabel text classification of public procurements using deep learning intent detection ; Textklassificering av offentliga upphandlingar med djupa artificiella neuronnät och avsåtsdetektering
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LSTM vs Random Forest for Binary Classification of Insurance Related Text ; LSTM vs Random Forest för binär klassificering av försäkringsrelaterad text
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Public Sentiment on Twitter and Stock Performance : A Study in Natural Language Processing ; Allmänna sentimentet på Twitter och aktiemarknaden : En studie i språkteknologi
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| 15 |
Integration of Migrant Populations into Health Monitoring in Germany: Results from a Feasibility Study
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In: Survey Methods: Insights from the Field ; 1-11 ; Probability and Nonprobability Sampling: Sampling of hard-to-reach survey populations (2019)
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FREDDIE Shiny - an online statistics interface ; FREDDIE Shiny - ein Online-Werkzeug für Statistik
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| 19 |
Supervised and unsupervised methods for learning representations of linguistic units
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| 20 |
Functional linear mixed models for complex correlation structures and general sampling grids
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