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        <title>Using Sublexical Translations to Handle the OOV Problem in MT</title>
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        <namePart type="given">Chung-chi</namePart>
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        <namePart type="given">Ho-ching</namePart>
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    <name type="personal">
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        <dateIssued>2010-oct&quot; 31-&quot;nov&quot; 4&quot;</dateIssued>
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            <title>Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers</title>
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            <publisher>Association for Machine Translation in the Americas</publisher>
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    <abstract>We introduce a method for learning to translate out-of-vocabulary (OOV) words. The method focuses on combining sublexical/constituent translations of an OOV to generate its translation candidates. In our approach, wild-card searches are formulated based on our OOV analysis, aimed at maximizing the probability of retrieving OOVs’ sublexical translations from existing resource of machine translation (MT) systems. At run-time, translation candidates of the unknown words are generated from their suitable sublexical translations and ranked based on monolingual and bilingual information. We have incorporated the OOV model into a state-of-the-art MT system and experimental results show that our model indeed helps to ease the negative impact of OOVs on translation quality, especially for sentences containing more OOVs (significant improvement).</abstract>
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        <url>https://aclanthology.org/2010.amta-papers.13</url>
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%0 Conference Proceedings
%T Using Sublexical Translations to Handle the OOV Problem in MT
%A Huang, Chung-chi
%A Yen, Ho-ching
%A Huang, Shih-ting
%A Chang, Jason
%S Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers
%D 2010
%8 oct" 31 "nov" 4"
%I Association for Machine Translation in the Americas
%C Denver, Colorado, USA
%F huang-etal-2010-using
%X We introduce a method for learning to translate out-of-vocabulary (OOV) words. The method focuses on combining sublexical/constituent translations of an OOV to generate its translation candidates. In our approach, wild-card searches are formulated based on our OOV analysis, aimed at maximizing the probability of retrieving OOVs’ sublexical translations from existing resource of machine translation (MT) systems. At run-time, translation candidates of the unknown words are generated from their suitable sublexical translations and ranked based on monolingual and bilingual information. We have incorporated the OOV model into a state-of-the-art MT system and experimental results show that our model indeed helps to ease the negative impact of OOVs on translation quality, especially for sentences containing more OOVs (significant improvement).
%U https://aclanthology.org/2010.amta-papers.13

Markdown (Informal)

[Using Sublexical Translations to Handle the OOV Problem in MT](https://aclanthology.org/2010.amta-papers.13) (Huang et al., AMTA 2010)

• Using Sublexical Translations to Handle the OOV Problem in MT (Huang et al., AMTA 2010)

ACL

• Chung-chi Huang, Ho-ching Yen, Shih-ting Huang, and Jason Chang. 2010. Using Sublexical Translations to Handle the OOV Problem in MT. In Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers, Denver, Colorado, USA. Association for Machine Translation in the Americas.