| 研究開始時の研究の概要 |
I will conduct unsupervised neural machine translation (NMT) in universal scenarios.
1. In the scenario of rich-resource languages, the abundant monolingual corpora are available. I will generate high-quality pseudo parallel data by back-translation.
2. In the scenario of low-resource languages, the monolingual corpora are rare. I will conduct high-quality bilingual word embedding for robust UNMT.
3. In the real-scenario translation, the domain of user query (test data) is difficult to predict and is sometimes different from the training data. I will adapt the UNMT according to user queries.
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| 研究実績の概要 |
I have proposed a universal unsupervised approach which train the translation model without using any parallel data. Compared with the existing unsupervised neural machine translation (UNMT) methods, which has only been applied to similar or rich-resource language pairs, my methods can be adapted to universal scenarios. I have published more than 20 peer-reviewed research papers (I am the corresponding authors of most of these papers). Most of these papers are published in the top-tier conferences and journal. Such as 7 ACL, 1 EMNLP, 2 AAAI, 2 ICLR, and 3 IEEE/ACM transactions. I won several first places in top-tier MT/NLP shared tasks, such as WMT-2019, WMT-2020, CoNLL-2019, etc.
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