Papers by Benjamin Marie
Disfluency Generation for More Robust Dialogue Systems (2023.findings-acl)
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| Challenge: | Disfluencies in user utterances can trigger a chain of errors impacting all the modules of a dialogue system. |
| Approach: | They propose to augment existing dialogue datasets with disfluent utterances by paraphrasing them into disfluente ones. |
| Outcome: | The proposed method improves dialogue state tracking and response generation by combining disfluent utterances with disfluency utteraces. |
Unsupervised Extraction of Partial Translations for Neural Machine Translation (N19-1)
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| Challenge: | Neural machine translation systems usually require a large quantity of bilingual parallel data for training. |
| Approach: | They propose an algorithm for extracting from monolingual data what they call partial translations . partial translation is a pair of source and target sentences that contain sequences of tokens that are translations of each other. |
| Outcome: | The proposed algorithm extracts from monolingual data what we call partial translations . it takes only source and target monolingual datasets as input . |
Synthesizing Parallel Data of User-Generated Texts with Zero-Shot Neural Machine Translation (2020.tacl-1)
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| Challenge: | Neural machine translation systems are usually trained on clean parallel data, but the quality of translations is poor when translating noisy texts. |
| Approach: | They synthesize parallel data of UGT and exploit monolingual data to generate translations . they propose to use monolingual parallel data to train or adapt NMT systems . |
| Outcome: | The proposed approach improves the translation quality of noisy texts while making them more robust. |
Supervised and Unsupervised Machine Translation for Myanmar-English and Khmer-English (D19-52)
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Benjamin Marie, Hour Kaing, Aye Myat Mon, Chenchen Ding, Atsushi Fujita, Masao Utiyama, Eiichiro Sumita
| Challenge: | Using cleaned and normalized noisy monolingual data, supervised neural and statistical machine translation systems performed among the best for the four translation directions. |
| Approach: | They present supervised and unsupervised machine translation systems for the WAT2019 Myanmar-English and Khmer-English translation tasks. |
| Outcome: | The proposed systems performed among the best for the four translation directions. |
Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers (2021.acl-long)
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| Challenge: | a meta-evaluation of machine translation (MT) has been conducted in 769 research papers . a recent study shows that evaluation practices have changed over the past decade . |
| Approach: | They propose a meta-evaluation method for machine translation that uses BLEU scores to evaluate MT performance. |
| Outcome: | The proposed meta-evaluation of machine translation shows that evaluation practices have changed over the past decade . the authors suggest that the evaluation process should be streamlined and standardized to ensure the validity of the evaluation method . |
Unsupervised Joint Training of Bilingual Word Embeddings (P19-1)
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| Challenge: | Existing methods for unsupervised bilingual word embeddings are limited by the dissimilarity between the word embedded spaces. |
| Approach: | They propose a method that trains unsupervised bilingual word embeddings jointly on parallel data generated through unsupervised machine translation. |
| Outcome: | The proposed method outperforms unsupervised mapped bilingual word embeddings in cross-lingual NLP tasks. |
Tagged Back-translation Revisited: Why Does It Really Work? (2020.acl-main)
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| Challenge: | In this paper, we show that neural machine translation systems trained on large back-translated data overfit some of the characteristics of machine-transcribed texts. |
| Approach: | They propose to add a tag to back-translations to help distinguish back-translated data from original parallel training data. |
| Outcome: | The proposed tag helps the system distinguish back-translated data from original parallel training data and is as effective as a tag in high-resource training. |