Papers by Isao Goto
Content-Equivalent Translated Parallel News Corpus and Extension of Domain Adaptation for NMT (2020.lrec-1)
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| Challenge: | Existing methods to train NMT systems with noisy data are not sufficient . a recent increase in foreigners visiting Japan has created a significant information gap . |
| Approach: | They propose a Japanese-English parallel news corpus that is content-equivalent . they extend a domain-adaptation method to train NMT models with clean corpus . |
| Outcome: | The proposed corpus improves translation quality and is more effective than existing methods. |
Neural Machine Translation System using a Content-equivalently Translated Parallel Corpus for the Newswire Translation Tasks at WAT 2019 (D19-52)
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| Challenge: | In addition to the JIJI Corpus, we developed a corpus of 0.22M sentence pairs by manually, translating Japanese news sentences into English content- equivalently. |
| Approach: | They propose to use JIJI Corpus and Equivalent-style sentences to translate Japanese news sentences into English content- equivalently. |
| Outcome: | The proposed translation models achieved the best human evaluation scores in the newswire translation tasks at WAT 2019 . they used the JIJI Corpus, which was provided by the task organizer, and the Equivalent-style translation model to translate Japanese news sentences into English content- equivalently. |
Domain Adaptation of Image Encoder for Multimodal Manga Translation (2026.eacl-srw)
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| Challenge: | Existing machine translation systems lack sufficient manga comprehension capabilities when utilizing image information. |
| Approach: | They propose a domain-adapted image encoder training method for manga . the method trains encoders to acquire visual features that consider the structural and sequential characteristics of the manga based on a Japanese-English translation task. |
| Outcome: | The proposed method improves translation evaluation metrics in Japanese-English translation task compared to the conventional method . |
Probabilistic Bilingual Subword Segmentation with Latent Subword Alignment (2026.eacl-srw)
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| Challenge: | Existing methods do not consider parallel relationships, preventing translation model training. |
| Approach: | They propose a method for learning subword correspondences in parallel sentence pairs using the EM algorithm. |
| Outcome: | The proposed method improves translation accuracy for many tasks. |
Overview of the 6th Workshop on Asian Translation (D19-52)
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Toshiaki Nakazawa, Nobushige Doi, Shohei Higashiyama, Chenchen Ding, Raj Dabre, Hideya Mino, Isao Goto, Win Pa Pa, Anoop Kunchukuttan, Yusuke Oda, Shantipriya Parida, Ondřej Bojar, Sadao Kurohashi
| Challenge: | The 6th workshop on Asian translation (WAT2019) was held in hong kong, hongkong, and hong kong. |
| Approach: | They present the results of the shared tasks from the 6th workshop on Asian translation (WAT2019) 25 teams participated in the shared task and 10 research paper submissions were accepted . |
| Outcome: | The results of the 6th workshop on Asian translation (WAT2019) include JaEn, JaZh scientific paper translation subtasks, Ja'En, ja'Ko, Ja’En patent translation sub tasks, Hi'En and My'En patent subtask and Ru'Ja news commentary translation task. |
Effective Use of Target-side Context for Neural Machine Translation (2020.coling-main)
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| Challenge: | Existing methods to train NMT systems with noisy data are not sufficient . et al., 2018) found that NMT models can learn with multiple types of corpora . |
| Approach: | They propose a Japanese-English news corpus that is content-equivalent . they extend a domain-adaptation method to train NMT models with clean corpus . |
| Outcome: | The proposed corpus improves translation quality and is more efficient than existing methods. |