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    <abstract>We investigate pivot-based translation between related languages in a low resource, phrase-based SMT setting. We show that a subword-level pivot-based SMT model using a related pivot language is substantially better than word and morpheme-level pivot models. It is also highly competitive with the best direct translation model, which is encouraging as no direct source-target training corpus is used. We also show that combining multiple related language pivot models can rival a direct translation model. Thus, the use of subwords as translation units coupled with multiple related pivot languages can compensate for the lack of a direct parallel corpus.</abstract>
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%0 Conference Proceedings
%T Utilizing Lexical Similarity between Related, Low-resource Languages for Pivot-based SMT
%A Kunchukuttan, Anoop
%A Shah, Maulik
%A Prakash, Pradyot
%A Bhattacharyya, Pushpak
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F kunchukuttan-etal-2017-utilizing
%X We investigate pivot-based translation between related languages in a low resource, phrase-based SMT setting. We show that a subword-level pivot-based SMT model using a related pivot language is substantially better than word and morpheme-level pivot models. It is also highly competitive with the best direct translation model, which is encouraging as no direct source-target training corpus is used. We also show that combining multiple related language pivot models can rival a direct translation model. Thus, the use of subwords as translation units coupled with multiple related pivot languages can compensate for the lack of a direct parallel corpus.
%U https://aclanthology.org/I17-2048
%P 283-289

Markdown (Informal)

[Utilizing Lexical Similarity between Related, Low-resource Languages for Pivot-based SMT](https://aclanthology.org/I17-2048) (Kunchukuttan et al., IJCNLP 2017)

• Utilizing Lexical Similarity between Related, Low-resource Languages for Pivot-based SMT (Kunchukuttan et al., IJCNLP 2017)

ACL

• Anoop Kunchukuttan, Maulik Shah, Pradyot Prakash, and Pushpak Bhattacharyya. 2017. Utilizing Lexical Similarity between Related, Low-resource Languages for Pivot-based SMT. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 283–289, Taipei, Taiwan. Asian Federation of Natural Language Processing.