Mikio Yamamoto


2017

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Patent NMT integrated with Large Vocabulary Phrase Translation by SMT at WAT 2017
Zi Long | Ryuichiro Kimura | Takehito Utsuro | Tomoharu Mitsuhashi | Mikio Yamamoto
Proceedings of the 4th Workshop on Asian Translation (WAT2017)

Neural machine translation (NMT) cannot handle a larger vocabulary because the training complexity and decoding complexity proportionally increase with the number of target words. This problem becomes even more serious when translating patent documents, which contain many technical terms that are observed infrequently. Long et al.(2017) proposed to select phrases that contain out-of-vocabulary words using the statistical approach of branching entropy. The selected phrases are then replaced with tokens during training and post-translated by the phrase translation table of SMT. In this paper, we apply the method proposed by Long et al. (2017) to the WAT 2017 Japanese-Chinese and Japanese-English patent datasets. Evaluation on Japanese-to-Chinese, Chinese-to-Japanese, Japanese-to-English and English-to-Japanese patent sentence translation proved the effectiveness of phrases selected with branching entropy, where the NMT model of Long et al.(2017) achieves a substantial improvement over a baseline NMT model without the technique proposed by Long et al.(2017).

2016

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Translation of Patent Sentences with a Large Vocabulary of Technical Terms Using Neural Machine Translation
Zi Long | Takehito Utsuro | Tomoharu Mitsuhashi | Mikio Yamamoto
Proceedings of the 3rd Workshop on Asian Translation (WAT2016)

Neural machine translation (NMT), a new approach to machine translation, has achieved promising results comparable to those of traditional approaches such as statistical machine translation (SMT). Despite its recent success, NMT cannot handle a larger vocabulary because training complexity and decoding complexity proportionally increase with the number of target words. This problem becomes even more serious when translating patent documents, which contain many technical terms that are observed infrequently. In NMTs, words that are out of vocabulary are represented by a single unknown token. In this paper, we propose a method that enables NMT to translate patent sentences comprising a large vocabulary of technical terms. We train an NMT system on bilingual data wherein technical terms are replaced with technical term tokens; this allows it to translate most of the source sentences except technical terms. Further, we use it as a decoder to translate source sentences with technical term tokens and replace the tokens with technical term translations using SMT. We also use it to rerank the 1,000-best SMT translations on the basis of the average of the SMT score and that of the NMT rescoring of the translated sentences with technical term tokens. Our experiments on Japanese-Chinese patent sentences show that the proposed NMT system achieves a substantial improvement of up to 3.1 BLEU points and 2.3 RIBES points over traditional SMT systems and an improvement of approximately 0.6 BLEU points and 0.8 RIBES points over an equivalent NMT system without our proposed technique.

2015

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Evaluating Features for Identifying Japanese-Chinese Bilingual Synonymous Technical Terms from Patent Families
Zi Long | Takehito Utsuro | Tomoharu Mitsuhashi | Mikio Yamamoto
Proceedings of the Eighth Workshop on Building and Using Comparable Corpora

2014

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Word Order Does NOT Differ Significantly Between Chinese and Japanese
Chenchen Ding | Masao Utiyama | Eiichiro Sumita | Mikio Yamamoto
Proceedings of the 1st Workshop on Asian Translation (WAT2014)

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Automatic News Source Detection in Twitter Based on Text Segmentation
Takashi Inui | Masaki Saito | Mikio Yamamoto
Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing

2013

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An Efficient Language Model Using Double-Array Structures
Makoto Yasuhara | Toru Tanaka | Jun-ya Norimatsu | Mikio Yamamoto
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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An Unsupervised Parameter Estimation Algorithm for a Generative Dependency N-gram Language Model
Chenchen Ding | Mikio Yamamoto
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2011

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Applying Sentiment-oriented Sentence Filtering to Multilingual Review Classification
Takashi Inui | Mikio Yamamoto
Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP 2011)

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Semi-Automatic Identification of Bilingual Synonymous Technical Terms from Phrase Tables and Parallel Patent Sentences
Bing Liang | Takehito Utsuro | Mikio Yamamoto
Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation

2008

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Producing a Test Collection for Patent Machine Translation in the Seventh NTCIR Workshop
Atsushi Fujii | Masao Utiyama | Mikio Yamamoto | Takehito Utsuro
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

In aiming at research and development on machine translation, we produced a test collection for Japanese-English machine translation in the seventh NTCIR Workshop. This paper describes details of our test collection. From patent documents published in Japan and the United States, we extracted patent families as a parallel corpus. A patent family is a set of patent documents for the same or related invention and these documents are usually filed to more than one country in different languages. In the parallel corpus, we aligned Japanese sentences with their counterpart English sentences. Our test collection, which includes approximately 2,000,000 sentence pairs, can be used to train and test machine translation systems. Our test collection also includes search topics for cross-lingual patent retrieval and the contribution of machine translation to a patent retrieval task can also be evaluated. Our test collection will be available to the public for research purposes after the NTCIR final meeting.

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Sentiment Analysis Based on Probabilistic Models Using Inter-Sentence Information
Kugatsu Sadamitsu | Satoshi Sekine | Mikio Yamamoto
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

This paper proposes a new method of the sentiment analysis utilizing inter-sentence structures especially for coping with reversal phenomenon of word polarity such as quotation of other’s opinions on an opposite side. We model these phenomenon using Hidden Conditional Random Fields(HCRFs) with three kinds of features: transition features, polarity features and reversal (of polarity) features. Polarity features and reversal features are doubly added to each word, and each weight of the features are trained by the common structure of positive and negative corpus in, for example, assuming that reversal phenomenon occured for the same reason (features) in both polarity corpus. Our method achieved better accuracy than the Naive Bayes method and as good as SVMs.

2006

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Relevance Feedback Models for Recommendation
Masao Utiyama | Mikio Yamamoto
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

2002

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The Present Status of Speech Database in Japan: Development, Management, and Application to Speech Research
Hisao Kuwabara | Shuich Itahashi | Mikio Yamamoto | Toshiyuki Takezawa | Satoshi Nakamura | Kazuya Takeda
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

2001

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Using Suffix Arrays to Compute Term Frequency and Document Frequency for All Substrings in a Corpus
Mikio Yamamoto | Kenneth W. Church
Computational Linguistics, Volume 27, Number 1, March 2001

2000

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IPA Japanese Dictation Free Software Project
Katsunobu Itou | Kiyohiro Shikano | Tatsuya Kawahara | Kasuya Takeda | Atsushi Yamada | Akinori Itou | Takehito Utsuro | Tetsunori Kobayashi | Nobuaki Minematsu | Mikio Yamamoto | Shigeki Sagayama | Akinobu Lee
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

1998

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Using Suffix Arrays to Compute Term Frequency and Document Frequency for All Substrings in a Corpus
Mikio Yamamoto | Kenneth W. Church
Sixth Workshop on Very Large Corpora

1996

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A Re-estimation Method for Stochastic Language Modeling from Ambiguous Observations
Mikio Yamamoto
Fourth Workshop on Very Large Corpora