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Published in UC San Francisco, Department of Testing, 2012
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Published in UC-Berkeley Institute for Testing Science, 2013
Published in London School of Testing, 2014
Published in Testing Institute of America 2014 Annual Conference, 2014
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Published in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020
Sparse language vectors from linguistic typology databases and learned embeddings from tasks like multilingual machine translation have been investigated in isolation, without analysing how they could benefit from each other’s language characterisation. We propose to fuse both views using singular vector canonical correlation analysis and study what kind of information is induced from each source. By inferring typological features and language phylogenies, we observe that our representations embed typology and strengthen correlations with language relationships. We then take advantage of our multi-view language vector space for multilingual machine translation, where we achieve competitive overall translation accuracy in tasks that require information about language similarities, such as language clustering and ranking candidates for multilingual transfer. With our method, we can easily project and assess new languages without expensive retraining of massive multilingual or ranking models, which are major disadvantages of related approaches.
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Published in , 2022
We present a survey covering the state of the art in low-resource machine translation research. There are currently around 7000 languages spoken in the world and almost all language pairs lack significant resources for training machine translation models. There has been increasing interest in research addressing the challenge of producing useful translation models when very little translated training data is available.We present a summary of this topical research field and provide a description of the techniques evaluated by researchers in several recent shared tasks in low-resource MT.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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