Satoru Ikehara


2009

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Statistical machine translation adding pattern-based machine translation in Chinese-English translation
Jin’ichi Murakami | Masato Tokuhisa | Satoru Ikehara
Proceedings of the 6th International Workshop on Spoken Language Translation: Evaluation Campaign

We have developed a two-stage machine translation (MT) system. The first stage is a rule-based machine translation system. The second stage is a normal statistical machine translation system. For Chinese-English machine translation, first, we used a Chinese-English rule-based MT, and we obtained ”ENGLISH” sentences from Chinese sentences. Second, we used a standard statistical machine translation. This means that we translated ”ENGLISH” to English machine translation. We believe this method has two advantages. One is that there are fewer unknown words. The other is that it produces structured or grammatically correct sentences. From the results of experiments, we obtained a BLEU score of 0.3151 in the BTEC-CE task using our proposed method. In contrast, we obtained a BLEU score of 0.3311 in the BTEC-CE task using a standard method (moses). This means that our proposed method was not as effective for the BTEC-CE task. Therefore, we will try to improve the performance by optimizing parameters.

2008

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Non-Compositional Language Model and Pattern Dictionary Development for Japanese Compound and Complex Sentences
Satoru Ikehara | Masato Tokuhisa | Jin’ichi Murakami
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Statistical machine translation without long parallel sentences for training data.
Jin’ichi Murakami | Masato Tokuhisa | Satoru Ikehara
Proceedings of the 5th International Workshop on Spoken Language Translation: Evaluation Campaign

In this study, we paid attention to the reliability of phrase table. We have been used the phrase table using Och’s method[2]. And this method sometimes generate completely wrong phrase tables. We found that such phrase table caused by long parallel sentences. Therefore, we removed these long parallel sentences from training data. Also, we utilized general tools for statistical machine translation, such as ”Giza++”[3], ”moses”[4], and ”training-phrase-model.perl”[5]. We obtained a BLEU score of 0.4047 (TEXT) and 0.3553(1-BEST) of the Challenge-EC task for our proposed method. On the other hand, we obtained a BLEU score of 0.3975(TEXT) and 0.3482(1-BEST) of the Challenge-EC task for a standard method. This means that our proposed method was effective for the Challenge-EC task. However, it was not effective for the BTECT-CE and Challenge-CE tasks. And our system was not good performance. For example, our system was the 7th place among 8 system for Challenge-EC task.

2007

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Statistical machine translation using large J/E parallel corpus and long phrase tables
Jin’ichi Murakami | Masato Tokuhisa | Satoru Ikehara
Proceedings of the Fourth International Workshop on Spoken Language Translation

Our statistical machine translation system that uses large Japanese-English parallel sentences and long phrase tables is described. We collected 698,973 Japanese-English parallel sentences, and we used long phrase tables. Also, we utilized general tools for statistical machine translation, such as ”Giza++”[1], ”moses”[2], and ”training-phrasemodel.perl”[3]. We used these data and these tools, We challenge the contest for IWSLT07. In which task was the result (0.4321 BLEU) obtained.

2006

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Building Carefully Tagged Bilingual Corpora to Cope with Linguistic Idiosyncrasy
Yoshihiko Nitta | Masashi Saraki | Satoru Ikehara
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

We illustrate the effectiveness of medium-sized carefully tagged bilingual core corpus, that is, “semantic typology patterns” in our term together with some examples to give concrete evidence of its usefulness. The most important characteristic of these semantic typology patterns is the bridging mechanism between two languages which is based on sequences syntactic codes and semantic codes. This characteristic gives both wide coverage and flexible applicability of core bilingual core corpus though its volume size is not so large. A further work is to be done for grasping some intuitive feeling of pertinent coarseness and fineness of patterns. Here coarseness feeling is concerning the generalization in phrase-level and clause-level semantic patterns and fineness is concerning word-level semantic patterns. Based on this feeling we will complete the core tagged bilingual corpora while enhancing the necessary support functions and utilities.

2002

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Toward the realization of typological semantic pattern dictionaries for MT
Satoru Ikehara
Workshop on machine translation roadmap

1999

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Automatic generation of semantic dependency rules for Japanese noun phrases with particles “no”
Satoru Ikehara | Shinnji Nakai | Jin’ichi Murakami
Proceedings of the 8th Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages

1996

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Classifiers in Japanese-to-English Machine Translation
Francis Bond | Kentaro Ogura | Satoru Ikehara
COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics

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Learning Bilingual Collocations by Word-Level Sorting
Masahiko Haruno | Satoru Ikehara | Takefumi Yamazaki
COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics

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A Statistical Method for Extracting Uninterrupted and Interrupted Collocations from Very Large Corpora
Satoru Ikehara | Satoshi Shirai | Hajime Uchino
COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics

1995

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Intrasentential Resolution of Japanese Zero Pronouns in a Machine Translation System using Semantic and Pragmatic Constraints
Hiromi Nakaiwa | Satoru Ikehara
Proceedings of the Sixth Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages

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A Method for Automatically Adapting an MT System to Different Domains
Setsuo Yamada | Hiromi Nakaiwa | Kentaro Ogura | Satoru Ikehara
Proceedings of the Sixth Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages

1994

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Automatic Aquisition of Semantic Attributes for User Defined Words m Japanese to English Machine Translation
Satoru Ikehara | Satoshi Shirai | Akio Yokoo | Francis Bond | Yoshie Omi
Fourth Conference on Applied Natural Language Processing

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English Adverb Generation in Japanese to English Machine Translation
Kentaro Ogura | Francis Bond | Satoru Ikehara
Fourth Conference on Applied Natural Language Processing

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An Evaluation of a Method to Detect and Correct Erroneous Characters in Japanese input through an OCR using Markov Models
Tetsuo Araki | Satoru Ikehara | Nobuyuki Tsukahara | Yasunori Komatsu
Fourth Conference on Applied Natural Language Processing

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Countability and Number in Japanese to English Machine Translation
Francis Bond | Kentaro Ogura | Satoru Ikehara
COLING 1994 Volume 1: The 15th International Conference on Computational Linguistics

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An Evaluation to Detect and Correct Erroneous Characters Wrongly Substituted, Deleted and Inserted in Japanese and English Sentences Using Markov Models
Tetsuo Araki | Satoru Ikehara | Nobuyuki Tsukahara | Yasunori Komatsu
COLING 1994 Volume 1: The 15th International Conference on Computational Linguistics

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A System of Verbal Semantic Attributes Focused on the Syntactic Correspondence between Japanese and English
Hiromi Nakaiwa | Akio Yokoo | Satoru Ikehara
COLING 1994 Volume 2: The 15th International Conference on Computational Linguistics

1993

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Effects of Automatic Rewriting of Source Language within a Japanese to English MT System
Satoshi Shirai | Satoru Ikehara | Tsukasa Kawaoka
Proceedings of the Fifth Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages

1992

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Zero Pronoun Resolution in a Machine Translation System by using Japanese to English Verbal Semantic Attributes.
Hiromi Nakaiwa | Satoru Ikehara
Third Conference on Applied Natural Language Processing

1991

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Toward an MT System without Pre-Editing: Effects of a New Method in ALT-J/E
Satoru Ikehara | Satoshi Shirai | Akio Yokoo | Hiromi Nakaiwa
Proceedings of Machine Translation Summit III: Papers

Recently, several types of Japanese to English MT (machine translation) systems have been developed, but prior to using such systems, they have required a pre-editing process of re-writing the original text into Japanese that could be easily translated. For communication of translated information requiring speed in dissemination, application of these systems would necessarily pose problems. To overcome such problems, a Multi-Level Translation Method based on Constructive Process Theory had been proposed. In this paper, the benefits of this method in ALT-J/E will be described. In comparison with the conventional elementary composition method, the Multi-Level Translation Method, emphasizing the importance of the meaning contained in expression structures, has been ascertained to be capable of conducting translation according to meaning and context processing with comparative ease. We are now hopeful of realizing machine translation omitting the process of pre-editing.