Hideya Mino


2024

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Understanding How Positional Encodings Work in Transformer Model
Taro Miyazaki | Hideya Mino | Hiroyuki Kaneko
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

A transformer model is used in general tasks such as pre-trained language models and specific tasks including machine translation. Such a model mainly relies on positional encodings (PEs) to handle the sequential order of input vectors. There are variations of PEs, such as absolute and relative, and several studies have reported on the superiority of relative PEs. In this paper, we focus on analyzing in which part of a transformer model PEs work and the different characteristics between absolute and relative PEs through a series of experiments. Experimental results indicate that PEs work in both self- and cross-attention blocks in a transformer model, and PEs should be added only to the query and key of an attention mechanism, not to the value. We also found that applying two PEs in combination, a relative PE in the self-attention block and an absolute PE in the cross-attention block, can improve translation quality.

2023

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Proceedings of the 10th Workshop on Asian Translation
Toshiaki Nakazawa | Kazutaka Kinugawa | Hideya Mino | Isao Goto | Raj Dabre | Shohei Higashiyama | Shantipriya Parida | Makoto Morishita | Ondrej Bojar | Akiko Eriguchi | Yusuke Oda | Akiko Eriguchi | Chenhui Chu | Sadao Kurohashi
Proceedings of the 10th Workshop on Asian Translation

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Overview of the 10th Workshop on Asian Translation
Toshiaki Nakazawa | Kazutaka Kinugawa | Hideya Mino | Isao Goto | Raj Dabre | Shohei Higashiyama | Shantipriya Parida | Makoto Morishita | Ondřej Bojar | Akiko Eriguchi | Yusuke Oda | Chenhui Chu | Sadao Kurohashi
Proceedings of the 10th Workshop on Asian Translation

This paper presents the results of the shared tasks from the 10th workshop on Asian translation (WAT2023). For the WAT2023, 2 teams submitted their translation results for the human evaluation. We also accepted 1 research paper. About 40 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.

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Leveraging the Fusion-in-Decoder for Label Classification
Azumi Okuda | Hideya Mino | Taro Miyazaki | Jun Goto
Proceedings of the Second Workshop on Information Extraction from Scientific Publications

2022

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Overview of the 9th Workshop on Asian Translation
Toshiaki Nakazawa | Hideya Mino | Isao Goto | Raj Dabre | Shohei Higashiyama | Shantipriya Parida | Anoop Kunchukuttan | Makoto Morishita | Ondřej Bojar | Chenhui Chu | Akiko Eriguchi | Kaori Abe | Yusuke Oda | Sadao Kurohashi
Proceedings of the 9th Workshop on Asian Translation

This paper presents the results of the shared tasks from the 9th workshop on Asian translation (WAT2022). For the WAT2022, 8 teams submitted their translation results for the human evaluation. We also accepted 4 research papers. About 300 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.

2021

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Proceedings of the 8th Workshop on Asian Translation (WAT2021)
Toshiaki Nakazawa | Hideki Nakayama | Isao Goto | Hideya Mino | Chenchen Ding | Raj Dabre | Anoop Kunchukuttan | Shohei Higashiyama | Hiroshi Manabe | Win Pa Pa | Shantipriya Parida | Ondřej Bojar | Chenhui Chu | Akiko Eriguchi | Kaori Abe | Yusuke Oda | Katsuhito Sudoh | Sadao Kurohashi | Pushpak Bhattacharyya
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

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Overview of the 8th Workshop on Asian Translation
Toshiaki Nakazawa | Hideki Nakayama | Chenchen Ding | Raj Dabre | Shohei Higashiyama | Hideya Mino | Isao Goto | Win Pa Pa | Anoop Kunchukuttan | Shantipriya Parida | Ondřej Bojar | Chenhui Chu | Akiko Eriguchi | Kaori Abe | Yusuke Oda | Sadao Kurohashi
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

This paper presents the results of the shared tasks from the 8th workshop on Asian translation (WAT2021). For the WAT2021, 28 teams participated in the shared tasks and 24 teams submitted their translation results for the human evaluation. We also accepted 5 research papers. About 2,100 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.

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NHK’s Lexically-Constrained Neural Machine Translation at WAT 2021
Hideya Mino | Kazutaka Kinugawa | Hitoshi Ito | Isao Goto | Ichiro Yamada | Takenobu Tokunaga
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

This paper describes the system of our team (NHK) for the WAT 2021 Japanese-English restricted machine translation task. In this task, the aim is to improve quality while maintaining consistent terminology for scientific paper translation. This task has a unique feature, where some words in a target sentence are given in addition to a source sentence. In this paper, we use a lexically-constrained neural machine translation (NMT), which concatenates the source sentence and constrained words with a special token to input them into the encoder of NMT. The key to the successful lexically-constrained NMT is the way to extract constraints from a target sentence of training data. We propose two extraction methods: proper-noun constraint and mistranslated-word constraint. These two methods consider the importance of words and fallibility of NMT, respectively. The evaluation results demonstrate the effectiveness of our lexical-constraint method.

2020

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Content-Equivalent Translated Parallel News Corpus and Extension of Domain Adaptation for NMT
Hideya Mino | Hideki Tanaka | Hitoshi Ito | Isao Goto | Ichiro Yamada | Takenobu Tokunaga
Proceedings of the Twelfth Language Resources and Evaluation Conference

In this paper, we deal with two problems in Japanese-English machine translation of news articles. The first problem is the quality of parallel corpora. Neural machine translation (NMT) systems suffer degraded performance when trained with noisy data. Because there is no clean Japanese-English parallel data for news articles, we build a novel parallel news corpus consisting of Japanese news articles translated into English in a content-equivalent manner. This is the first content-equivalent Japanese-English news corpus translated specifically for training NMT systems. The second problem involves the domain-adaptation technique. NMT systems suffer degraded performance when trained with mixed data having different features, such as noisy data and clean data. Though the existing methods try to overcome this problem by using tags for distinguishing the differences between corpora, it is not sufficient. We thus extend a domain-adaptation method using multi-tags to train an NMT model effectively with the clean corpus and existing parallel news corpora with some types of noise. Experimental results show that our corpus increases the translation quality, and that our domain-adaptation method is more effective for learning with the multiple types of corpora than existing domain-adaptation methods are.

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Effective Use of Target-side Context for Neural Machine Translation
Hideya Mino | Hitoshi Ito | Isao Goto | Ichiro Yamada | Takenobu Tokunaga
Proceedings of the 28th International Conference on Computational Linguistics

In this paper, we deal with two problems in Japanese-English machine translation of news articles. The first problem is the quality of parallel corpora. Neural machine translation (NMT) systems suffer degraded performance when trained with noisy data. Because there is no clean Japanese-English parallel data for news articles, we build a novel parallel news corpus consisting of Japanese news articles translated into English in a content-equivalent manner. This is the first content-equivalent Japanese-English news corpus translated specifically for training NMT systems. The second problem involves the domain-adaptation technique. NMT systems suffer degraded performance when trained with mixed data having different features, such as noisy data and clean data. Though the existing methods try to overcome this problem by using tags for distinguishing the differences between corpora, it is not sufficient. We thus extend a domain-adaptation method using multi-tags to train an NMT model effectively with the clean corpus and existing parallel news corpora with some types of noise. Experimental results show that our corpus increases the translation quality, and that our domain-adaptation method is more effective for learning with the multiple types of corpora than existing domain-adaptation methods are.

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Overview of the 7th Workshop on Asian Translation
Toshiaki Nakazawa | Hideki Nakayama | Chenchen Ding | Raj Dabre | Shohei Higashiyama | Hideya Mino | Isao Goto | Win Pa Pa | Anoop Kunchukuttan | Shantipriya Parida | Ondřej Bojar | Sadao Kurohashi
Proceedings of the 7th Workshop on Asian Translation

This paper presents the results of the shared tasks from the 7th workshop on Asian translation (WAT2020). For the WAT2020, 20 teams participated in the shared tasks and 14 teams submitted their translation results for the human evaluation. We also received 12 research paper submissions out of which 7 were accepted. About 500 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.

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Neural Machine Translation Using Extracted Context Based on Deep Analysis for the Japanese-English Newswire Task at WAT 2020
Isao Goto | Hideya Mino | Hitoshi Ito | Kazutaka Kinugawa | Ichiro Yamada | Hideki Tanaka
Proceedings of the 7th Workshop on Asian Translation

This paper describes the system of the NHK-NES team for the WAT 2020 Japanese–English newswire task. There are two main problems in Japanese-English news translation: translation of dropped subjects and compatibility between equivalent translations and English news-style outputs. We address these problems by extracting subjects from the context based on predicate-argument structures and using them as additional inputs, and constructing parallel Japanese-English news sentences equivalently translated from English news sentences. The evaluation results confirm the effectiveness of our context-utilization method.

2019

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Overview of the 6th Workshop on Asian Translation
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
Proceedings of the 6th Workshop on Asian Translation

This paper presents the results of the shared tasks from the 6th workshop on Asian translation (WAT2019) including Ja↔En, Ja↔Zh scientific paper translation subtasks, Ja↔En, Ja↔Ko, Ja↔En patent translation subtasks, Hi↔En, My↔En, Km↔En, Ta↔En mixed domain subtasks and Ru↔Ja news commentary translation task. For the WAT2019, 25 teams participated in the shared tasks. We also received 10 research paper submissions out of which 61 were accepted. About 400 translation results were submitted to the automatic evaluation server, and selected submis- sions were manually evaluated.

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Neural Machine Translation System using a Content-equivalently Translated Parallel Corpus for the Newswire Translation Tasks at WAT 2019
Hideya Mino | Hitoshi Ito | Isao Goto | Ichiro Yamada | Hideki Tanaka | Takenobu Tokunaga
Proceedings of the 6th Workshop on Asian Translation

This paper describes NHK and NHK Engineering System (NHK-ES)’s submission to the newswire translation tasks of WAT 2019 in both directions of Japanese→English and English→Japanese. In addition to the JIJI Corpus that was officially provided by the task organizer, we developed a corpus of 0.22M sentence pairs by manually, translating Japanese news sentences into English content- equivalently. The content-equivalent corpus was effective for improving translation quality, and our systems achieved the best human evaluation scores in the newswire translation tasks at WAT 2019.

2018

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Overview of the 5th Workshop on Asian Translation
Toshiaki Nakazawa | Katsuhito Sudoh | Shohei Higashiyama | Chenchen Ding | Raj Dabre | Hideya Mino | Isao Goto | Win Pa Pa | Anoop Kunchukuttan | Sadao Kurohashi
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation

2017

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Overview of the 4th Workshop on Asian Translation
Toshiaki Nakazawa | Shohei Higashiyama | Chenchen Ding | Hideya Mino | Isao Goto | Hideto Kazawa | Yusuke Oda | Graham Neubig | Sadao Kurohashi
Proceedings of the 4th Workshop on Asian Translation (WAT2017)

This paper presents the results of the shared tasks from the 4th workshop on Asian translation (WAT2017) including J↔E, J↔C scientific paper translation subtasks, C↔J, K↔J, E↔J patent translation subtasks, H↔E mixed domain subtasks, J↔E newswire subtasks and J↔E recipe subtasks. For the WAT2017, 12 institutions participated in the shared tasks. About 300 translation results have been submitted to the automatic evaluation server, and selected submissions were manually evaluated.

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A Target Attention Model for Neural Machine Translation
Hideya Mino | Andrew Finch | Eiichiro Sumita
Proceedings of Machine Translation Summit XVI: Research Track

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Key-value Attention Mechanism for Neural Machine Translation
Hideya Mino | Masao Utiyama | Eiichiro Sumita | Takenobu Tokunaga
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

In this paper, we propose a neural machine translation (NMT) with a key-value attention mechanism on the source-side encoder. The key-value attention mechanism separates the source-side content vector into two types of memory known as the key and the value. The key is used for calculating the attention distribution, and the value is used for encoding the context representation. Experiments on three different tasks indicate that our model outperforms an NMT model with a conventional attention mechanism. Furthermore, we perform experiments with a conventional NMT framework, in which a part of the initial value of a weight matrix is set to zero so that the matrix is as the same initial-state as the key-value attention mechanism. As a result, we obtain comparable results with the key-value attention mechanism without changing the network structure.

2016

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Proceedings of the 3rd Workshop on Asian Translation (WAT2016)
Toshiaki Nakazawa | Hideya Mino | Chenchen Ding | Isao Goto | Graham Neubig | Sadao Kurohashi | Ir. Hammam Riza | Pushpak Bhattacharyya
Proceedings of the 3rd Workshop on Asian Translation (WAT2016)

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Overview of the 3rd Workshop on Asian Translation
Toshiaki Nakazawa | Chenchen Ding | Hideya Mino | Isao Goto | Graham Neubig | Sadao Kurohashi
Proceedings of the 3rd Workshop on Asian Translation (WAT2016)

This paper presents the results of the shared tasks from the 3rd workshop on Asian translation (WAT2016) including J ↔ E, J ↔ C scientific paper translation subtasks, C ↔ J, K ↔ J, E ↔ J patent translation subtasks, I ↔ E newswire subtasks and H ↔ E, H ↔ J mixed domain subtasks. For the WAT2016, 15 institutions participated in the shared tasks. About 500 translation results have been submitted to the automatic evaluation server, and selected submissions were manually evaluated.

2015

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Proceedings of the 2nd Workshop on Asian Translation (WAT2015)
Toshiaki Nakazawa | Hideya Mino | Isao Goto | Graham Neubig | Sadao Kurohashi | Eiichiro Sumita
Proceedings of the 2nd Workshop on Asian Translation (WAT2015)

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Overview of the 2nd Workshop on Asian Translation
Toshiaki Nakazawa | Hideya Mino | Isao Goto | Graham Neubig | Sadao Kurohashi | Eiichiro Sumita
Proceedings of the 2nd Workshop on Asian Translation (WAT2015)

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Learning bilingual phrase representations with recurrent neural networks
Hideya Mino | Andrew Finch | Eiichiro Sumita
Proceedings of Machine Translation Summit XV: Papers

2014

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Syntax-Augmented Machine Translation using Syntax-Label Clustering
Hideya Mino | Taro Watanabe | Eiichiro Sumita
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Proceedings of the 1st Workshop on Asian Translation (WAT2014)
Toshiaki Nakazawa | Hideya Mino | Isao Goto | Sadao Kurohashi | Eiichiro Sumita
Proceedings of the 1st Workshop on Asian Translation (WAT2014)

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Overview of the 1st Workshop on Asian Translation
Toshiaki Nakazawa | Hideya Mino | Isao Goto | Sadao Kurohashi | Eiichiro Sumita
Proceedings of the 1st Workshop on Asian Translation (WAT2014)