Using syntax for improving phrase-based SMT in low-resource languages.
Data driven approaches for machine translation, such as statistical and neural machine translation, suffer from sparsity when dealing with low-resource languages. In these cases, using other sources of information including linguistic information could alleviate the problem. In this article, we focu...
| Publicado en: | Digital Scholarship in the Humanities Vol. 35; no. 3; pp. 507 - 529 |
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| Autores principales: | , |
| Formato: | Artículo |
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Oxford University Press / USA
Sep2020
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| 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=146172308&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 146172308 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Sep2020 vid: 35 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 146172308 10.1093/llc/fqz033 ppf: 507 ppct: 22 formats: fmt: – @attributes: type: T – @attributes: type: P size: 971KB tig: atl: Using syntax for improving phrase-based SMT in low-resource languages. aug: au: Fadaei, Hakimeh Faili, Heshaam affil: School of Electrical and Computer Engineering , College of Engineering, University of Tehran, Tehran, Iran School of Electrical and Computer Engineering , College of Engineering, University of Tehran, Tehran, Iran School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran su: Language & languages Information resources Translations Grammar sug: subj: Language & languages Information resources Translations Grammar ab: Data driven approaches for machine translation, such as statistical and neural machine translation, suffer from sparsity when dealing with low-resource languages. In these cases, using other sources of information including linguistic information could alleviate the problem. In this article, we focus on the problem of word ordering in translation from a high-resource to a low-resource language and try to improve the quality by using syntactic information from the high-resource side. We propose some syntactic features based on Tree Adjoining Grammar (TAG) to be employed in a phrase-based SMT model in order to improve the word ordering. In this work, a set of synchronous TAG rules is extracted and used to estimate the probability of the phrase orders suggested by the phrase-based model. The main idea of the article is to handle the word ordering by using the extended domain of locality property of TAG and abstracting the long distance dependencies into a local view, which is a TAG elementary tree. The experiments on English–Persian and English–German translation showed that, by combining the proposed TAG-based reordering features with lexical and hierarchical reordering models, we gain significant improvements over the baseline and in comparison with a neural reordering model and a pre-reordering model. 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: 2020 holdings: @attributes: islocal: N |
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