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Investigating query expansion and coreference resolution in question answering on BERT
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In: Bhattacharjee, Santanu, Haque, Rejwanul orcid:0000-0003-1680-0099 , Maillette de Buy Wenniger, Gideon and Way, Andy orcid:0000-0001-5736-5930 (2020) Investigating query expansion and coreference resolution in question answering on BERT. In: 25th International Conference on Natural Language & Information Systems (NLDB 2020)), 24 - 26 June 2020, Saarbrücken, Germany (Online). ISBN 978-3-030-51309-2 (2020)
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Improving transductive data selection algorithms for machine translation
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Poncelas, Alberto. - : Dublin City University. School of Computing, 2019. : Dublin City University. ADAPT, 2019
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In: Poncelas, Alberto orcid:0000-0002-5089-1687 (2019) Improving transductive data selection algorithms for machine translation. PhD thesis, Dublin City University. (2019)
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Combining SMT and NMT back-translated data for efficient NMT
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In: Poncelas, Alberto orcid:0000-0002-5089-1687 , Popović, Maja orcid:0000-0001-8234-8745 , Shterionov, Dimitar orcid:0000-0001-6300-797X , Maillette de Buy Wenniger, Gideon and Way, Andy orcid:0000-0001-5736-5930 (2019) Combining SMT and NMT back-translated data for efficient NMT. In: Recent Advances in Natural Language Processing (RANLP 2019), 2-4 Sept 2019, Varna, Bulgaria. (2019)
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Transductive data-selection algorithms for fine-tuning neural machine translation
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In: Poncelas, Alberto orcid:0000-0002-5089-1687 , Maillette de Buy Wenniger, Gideon orcid:0000-0001-8427-7055 and Way, Andy orcid:0000-0001-5736-5930 (2019) Transductive data-selection algorithms for fine-tuning neural machine translation. In: The 8th Workshop on Patent and Scientific Literature Translation, Dublin, Ireland. (2019)
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Adaptation of machine translation models with back-translated data using transductive data selection methods
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In: Poncelas, Alberto orcid:0000-0002-5089-1687 , Maillette de Buy Wenniger, Gideon orcid:0000-0001-8427-7055 and Way, Andy orcid:0000-0001-5736-5930 (2019) Adaptation of machine translation models with back-translated data using transductive data selection methods. In: A Proceedings of CICLing 2019, the 20th International Conference on Computational Linguistics and Intelligent Text Processing, 7 - 13 Apr 2019, La Rochelle, France. (2019)
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Abstract:
Data selection has proven its merit for improving Neural Machine Translation (NMT), when applied to authentic data. But the benefit of using synthetic data in NMT training, produced by the popular back-translation technique, raises the question if data selection could also be useful for synthetic data? In this work we use Infrequent n-gram Recovery (INR) and Feature Decay Algorithms (FDA), two transductive data selection methods to obtain subsets of sentences from synthetic data. These methods ensure that selected sentences share n-grams with the test set so the NMT model can be adapted to translate it. Performing data selection on back-translated data creates new challenges as the source-side may contain noise originated by the model used in the back-translation. Hence, finding ngrams present in the test set become more difficult. Despite that, in our work we show that adapting a model with a selection of synthetic data is an useful approach.
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Keyword:
Machine translating
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URL: http://doras.dcu.ie/23870/
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Applying N-gram alignment entropy to improve feature decay algorithms
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In: Poncelas, Alberto orcid:0000-0002-5089-1687 , Maillette de Buy Wenniger, Gideon and Way, Andy orcid:0000-0001-5736-5930 (2017) Applying N-gram alignment entropy to improve feature decay algorithms. The Prague Bulletin of Mathematical Linguistics (108). pp. 245-256. ISSN 0032-6585 (2017)
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Applying N-gram Alignment Entropy to Improve Feature Decay Algorithms
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 245-256 (2017) (2017)
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