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        <title>Machine Translation Evaluation for Arabic using Morphologically-enriched Embeddings</title>
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    <name type="personal">
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            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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    <abstract>Evaluation of machine translation (MT) into morphologically rich languages (MRL) has not been well studied despite posing many challenges. In this paper, we explore the use of embeddings obtained from different levels of lexical and morpho-syntactic linguistic analysis and show that they improve MT evaluation into an MRL. Specifically we report on Arabic, a language with complex and rich morphology. Our results show that using a neural-network model with different input representations produces results that clearly outperform the state-of-the-art for MT evaluation into Arabic, by almost over 75% increase in correlation with human judgments on pairwise MT evaluation quality task. More importantly, we demonstrate the usefulness of morpho-syntactic representations to model sentence similarity for MT evaluation and address complex linguistic phenomena of Arabic.</abstract>
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%0 Conference Proceedings
%T Machine Translation Evaluation for Arabic using Morphologically-enriched Embeddings
%A Guzmán, Francisco
%A Bouamor, Houda
%A Baly, Ramy
%A Habash, Nizar
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F guzman-etal-2016-machine
%X Evaluation of machine translation (MT) into morphologically rich languages (MRL) has not been well studied despite posing many challenges. In this paper, we explore the use of embeddings obtained from different levels of lexical and morpho-syntactic linguistic analysis and show that they improve MT evaluation into an MRL. Specifically we report on Arabic, a language with complex and rich morphology. Our results show that using a neural-network model with different input representations produces results that clearly outperform the state-of-the-art for MT evaluation into Arabic, by almost over 75% increase in correlation with human judgments on pairwise MT evaluation quality task. More importantly, we demonstrate the usefulness of morpho-syntactic representations to model sentence similarity for MT evaluation and address complex linguistic phenomena of Arabic.
%U https://aclanthology.org/C16-1132
%P 1398-1408

Markdown (Informal)

[Machine Translation Evaluation for Arabic using Morphologically-enriched Embeddings](https://aclanthology.org/C16-1132) (Guzmán et al., COLING 2016)

• Machine Translation Evaluation for Arabic using Morphologically-enriched Embeddings (Guzmán et al., COLING 2016)

ACL

• Francisco Guzmán, Houda Bouamor, Ramy Baly, and Nizar Habash. 2016. Machine Translation Evaluation for Arabic using Morphologically-enriched Embeddings. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1398–1408, Osaka, Japan. The COLING 2016 Organizing Committee.