著者

Shohei Higashiyama Masao Utiyama Eiichiro Sumita Masao Ideuchi Yoshiaki Oida Yohei Sakamoto Isaac Okada Yuji Matsumoto

出版者

The Association for Natural Language Processing

雑誌

自然言語処理 (ISSN:13407619)

巻号頁・発行日

vol.27, no.3, pp.499-530, 2020-09-15 (Released:2020-12-15)

参考文献数

54

被引用文献数

2

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Although limited effort has been devoted to exploring neural models in Japanese word segmentation, much effort has been actively applied to Chinese word segmentation because of the ability to minimize effort in feature engineering. In this work, we propose a character-based neural model that makes joint use of word information useful for disambiguating word boundaries. For each character in a sentence, our model uses an attention mechanism to estimate the importance of multiple candidate words that contain the character. Experimental results show that learning attention to proper words leads to accurate segmentations and that our model achieves better performance than existing statistical and neural models on both in-domain and cross-domain Japanese word segmentation datasets.

著者

Michael Paul Andrew Finch Eiichiro Sumita

出版者

The Association for Natural Language Processing

雑誌

自然言語処理 (ISSN:13407619)

巻号頁・発行日

vol.20, no.4, pp.563-583, 2013-09-13 (Released:2013-12-12)

参考文献数

29

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Recent research on multilingual statistical machine translation (SMT) focuses on the usage of pivot languages in order to overcome resource limitations for certain language pairs. This paper proposes a new method to translate a dialect language into a foreign language by integrating transliteration approaches based on Bayesian alignment (BA) models with pivot-based SMT approaches. The advantages of the proposed method with respect to standard SMT approaches are threefold: (1) it uses a standard language as the pivot language and acquires knowledge about the relation between dialects and a standard language automatically, (2) it avoids segmentation mismatches between the input and the translation model by mapping the character sequences of the dialect language to the word segmentation of the standard language, and (3) it reduces the translation task complexity by using monotone decoding techniques. Experiment results translating five Japanese dialects (Kumamoto, Kyoto, Nagoya, Okinawa, Osaka) into four Indo-European languages (English, German, Russian, Hindi) and two Asian languages (Chinese, Korean) revealed that the proposed method improves the translation quality of dialect translation tasks and outperforms standard pivot translation approaches concatenating SMT engines for the majority of the investigated language pairs.

著者

Shigeki MATSUDA Teruaki HAYASHI Yutaka ASHIKARI Yoshinori SHIGA Hidenori KASHIOKA Keiji YASUDA Hideo OKUMA Masao UCHIYAMA Eiichiro SUMITA Hisashi KAWAI Satoshi NAKAMURA

出版者

The Institute of Electronics, Information and Communication Engineers

雑誌

IEICE TRANSACTIONS on Information and Systems (ISSN:09168532)

巻号頁・発行日

vol.E100-D, no.4, pp.621-632, 2017-04-01

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This study introduces large-scale field experiments of VoiceTra, which is the world's first speech-to-speech multilingual translation application for smart phones. In the study, approximately 10 million input utterances were collected since the experiments commenced. The usage of collected data was analyzed and discussed. The study has several important contributions. First, it explains system configuration, communication protocol between clients and servers, and details of multilingual automatic speech recognition, multilingual machine translation, and multilingual speech synthesis subsystems. Second, it demonstrates the effects of mid-term system updates using collected data to improve an acoustic model, a language model, and a dictionary. Third, it analyzes system usage.