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