An Empirical Comparison of Parsers in Constraining Reordering for E-J Patent Machine Translation

• Item type: JInfP(1)
• 公開日: 2012-10-15
• タイトル: An Empirical Comparison of Parsers in Constraining Reordering for E-J Patent Machine Translation
• タイトル / 言語: en
• タイトル: An Empirical Comparison of Parsers in Constraining Reordering for E-J Patent Machine Translation
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [Regular Paper] patent translation, parser, comparison, reordering constraint, English to Japanese
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: National Institute of Information and Communications Technology
• 著者所属: National Institute of Information and Communications Technology
• 著者所属: National Institute of Information and Communications Technology
• 著者所属: National Institute of Information and Communications Technology
• 著者所属(英): en
• 著者所属(英): National Institute of Information and Communications Technology
• 著者所属(英): en
• 著者所属(英): National Institute of Information and Communications Technology
• 著者所属(英): en
• 著者所属(英): National Institute of Information and Communications Technology
• 著者所属(英): en
• 著者所属(英): National Institute of Information and Communications Technology
• 著者名: Isao, Goto; Masao, Utiyama; Takashi, Onishi; Eiichiro, Sumita
• 著者名(英): Isao, Goto; Masao, Utiyama; Takashi, Onishi; Eiichiro, Sumita
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Machine translation of patent documents is very important from a practical point of view. One of the key technologies for improving machine translation quality is the utilization of syntax. It is difficult to select the appropriate parser for English to Japanese patent machine translation because the effects of each parser on patent translation are not clear. This paper provides an empirical comparative evaluation of several state-of-the-art parsers for English, focusing on the effects on patent machine translation from English to Japanese. We add syntax to a method that constrains the reordering of noun phrases for phrase-based statistical machine translation. There are two methods for obtaining the noun phrases from input sentences: 1) an input sentence is directly parsed by a parser and 2) noun phrases from an input sentence are determined by a method using the parsing results of the context document that contains the input sentence. We measured how much each parser contributed to improving the translation quality for each of the two methods and how much a combination of parsers contributed to improving the translation quality for the second method. We conducted experiments using the NTCIR-8 patent translation task dataset. Most of the parsers improved translation quality. Combinations of parsers using the method based on context documents achieved the best translation quality.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Machine translation of patent documents is very important from a practical point of view. One of the key technologies for improving machine translation quality is the utilization of syntax. It is difficult to select the appropriate parser for English to Japanese patent machine translation because the effects of each parser on patent translation are not clear. This paper provides an empirical comparative evaluation of several state-of-the-art parsers for English, focusing on the effects on patent machine translation from English to Japanese. We add syntax to a method that constrains the reordering of noun phrases for phrase-based statistical machine translation. There are two methods for obtaining the noun phrases from input sentences: 1) an input sentence is directly parsed by a parser and 2) noun phrases from an input sentence are determined by a method using the parsing results of the context document that contains the input sentence. We measured how much each parser contributed to improving the translation quality for each of the two methods and how much a combination of parsers contributed to improving the translation quality for the second method. We conducted experiments using the NTCIR-8 patent translation task dataset. Most of the parsers improved translation quality. Combinations of parsers using the method based on context documents achieved the best translation quality.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA00700121
• 書誌情報: Journal of information processing; 巻 20, 号 4, p. 871-882, 発行日 2012-10-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-6652
• 出版者 / 言語: ja
• 出版者: 情報処理学会