| { |
| "paper_id": "Y10-1009", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T13:41:03.263421Z" |
| }, |
| "title": "Statistical Translation Model Based On Source Syntax Structure *", |
| "authors": [ |
| { |
| "first": "Qun", |
| "middle": [], |
| "last": "Liu", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "Chinese Academy of Sciences", |
| "location": { |
| "postCode": "100190", |
| "settlement": "Beijing", |
| "country": "P.R.China" |
| } |
| }, |
| "email": "liuqun@ict.ac.cn" |
| }, |
| { |
| "first": "Yang", |
| "middle": [], |
| "last": "Liu", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "Chinese Academy of Sciences", |
| "location": { |
| "postCode": "100190", |
| "settlement": "Beijing", |
| "country": "P.R.China" |
| } |
| }, |
| "email": "yliu@ict.ac.cn" |
| }, |
| { |
| "first": "Haitao", |
| "middle": [], |
| "last": "Mi", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "Chinese Academy of Sciences", |
| "location": { |
| "postCode": "100190", |
| "settlement": "Beijing", |
| "country": "P.R.China" |
| } |
| }, |
| "email": "htmi@ict.ac.cn" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "Syntax-based statistical translation model is proved to be better than phrasebased model, especially for language pairs with very different syntax structures, such as Chinese and English. In this talk I will introduce a serial of statistical translation models based on source syntax structure. The tree-based model uses the one best syntax tree for translation. The forest-based model uses a compact forest which encodes exponential number of syntax trees in a polynomial spaces and lead to better performance. The joint parsing and translation model produces source parse trees, using the source side of the translation rules instead of separate parsing rules, and generate translations on the target side simultaneously, which outperforms the forest-based model. Some extensions of these models are introduced also.", |
| "pdf_parse": { |
| "paper_id": "Y10-1009", |
| "_pdf_hash": "", |
| "abstract": [ |
| { |
| "text": "Syntax-based statistical translation model is proved to be better than phrasebased model, especially for language pairs with very different syntax structures, such as Chinese and English. In this talk I will introduce a serial of statistical translation models based on source syntax structure. The tree-based model uses the one best syntax tree for translation. The forest-based model uses a compact forest which encodes exponential number of syntax trees in a polynomial spaces and lead to better performance. The joint parsing and translation model produces source parse trees, using the source side of the translation rules instead of separate parsing rules, and generate translations on the target side simultaneously, which outperforms the forest-based model. Some extensions of these models are introduced also.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Abstract", |
| "sec_num": null |
| } |
| ], |
| "body_text": [], |
| "back_matter": [], |
| "bib_entries": { |
| "BIBREF0": { |
| "ref_id": "b0", |
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| "num": null, |
| "urls": [], |
| "raw_text": "Zhongjun He, Qun Liu, Shouxun Lin. Improving Statistical Machine Translation using Lexicalized Rule Selection. Proceedings of the 22nd International Conference on Computational Linguistics (COLING 2008), pages 321-328. Manchester, UK. Aug, 2008", |
| "links": null |
| }, |
| "BIBREF1": { |
| "ref_id": "b1", |
| "title": "Maximum Entropy based Rule Selection Model for Syntax-based Statistical Machine Translation", |
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| "raw_text": "Qun Liu, Zhongjun He, Yang Liu, Shouxun Lin. Maximum Entropy based Rule Selection Model for Syntax-based Statistical Machine Translation. Proceedings of EMNLP 2008, pp 89-97\uff0cHonolulu, Hawaii", |
| "links": null |
| }, |
| "BIBREF2": { |
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| "volume": "", |
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| "pages": "609--616", |
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| "num": null, |
| "urls": [], |
| "raw_text": "Yang Liu, Qun Liu, and Shouxun Lin. 2006. Tree-to-String Alignment Template for Statistical Machine Translation. Proceedings of COLING-ACL 2006, pp 609 -616, Sydney, Australia, July 17-21.", |
| "links": null |
| }, |
| "BIBREF3": { |
| "ref_id": "b3", |
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| "authors": [ |
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| "pages": "704--711", |
| "other_ids": {}, |
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| "urls": [], |
| "raw_text": "Yang Liu, Yun Huang, Qun Liu and Shouxun Lin, Forest-to-String Statistical Translation Rules, ACL2007, pp 704-711, Prague, Czech, June 2007", |
| "links": null |
| }, |
| "BIBREF4": { |
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| "pages": "558--566", |
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| "urls": [], |
| "raw_text": "Yang Liu, Yajuan L\u00fc and Qun Liu, Improving Tree-to-Tree Translation with Packed Forests. Proceedings of ACL-IJCNLP2009, pp 558-566, Singapore, August 2-7, 2009", |
| "links": null |
| }, |
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| }, |
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| "ref_entries": {} |
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