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arXiv · 1702.07203

Utilizing Lexical Similarity between Related, Low-resource Languages for Pivot-based SMT

Anoop Kunchukuttan·Maulik Shah·Pradyot Prakash·Pushpak Bhattacharyya
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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.

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Anoop Kunchukuttan, Maulik Shah, Pradyot Prakash, Pushpak Bhattacharyya. 2017-02-23. Utilizing Lexical Similarity between Related, Low-resource Languages for Pivot-based SMT. https://arxiv.org/abs/1702.07203

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