A preordering model based on phrasal dependency tree.
Intelligent machine translation (MT) is becoming an important field of research and development as the need for translations grows. Currently, the word reordering problem is one of the most important issues of MT systems. To tackle this problem, we present a source-side reordering method using phras...
| Publicado en: | Digital Scholarship in the Humanities Vol. 33; no. 4; pp. 748 - 766 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Oxford University Press / USA
Dec2018
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=132368566&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 132368566 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Dec2018 vid: 33 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 132368566 10.1093/llc/fqy009 ppf: 748 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P size: 905KB tig: atl: A preordering model based on phrasal dependency tree. aug: au: Farzi, Saeed Faili, Heshaam Kianian, Sahar affil: Faculty of Computer Engineering, K. N. Toosi University of Technology, Iran School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran Faculty of Computer Engineering, Shahid Rajaee Teacher Training, Iran su: Artificial intelligence Translating & interpreting Bilingualism Maximum likelihood statistics Order (Grammar) sug: subj: Artificial intelligence Translating & interpreting Bilingualism Maximum likelihood statistics Order (Grammar) ab: Intelligent machine translation (MT) is becoming an important field of research and development as the need for translations grows. Currently, the word reordering problem is one of the most important issues of MT systems. To tackle this problem, we present a source-side reordering method using phrasal dependency trees, which depict dependency relations between contiguous non-syntactic phrases. Reordering elements are automatically learned from a reordered phrasal dependency tree bank and are utilized to produce a source reordering lattice. The lattice finally is decoded by a monotone phrase-based SMT to translate a source sentence. The approach is evaluated on syntactically divergent language pairs, i.e. English→Persian and English→German, using the workshop of machine translation 2007 (WMT07) benchmark. The results demonstrate the superiority of the proposed method in terms of translation quality for both translation tasks. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2018 holdings: @attributes: islocal: N |
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