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

arXiv: https://arxiv.org/abs/1702.07203 · HTML: https://mlsanity.com/paper/1702.07203/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2017.

## Built on by (most cited first, 1 of 1)

- [Reconsidering SMT Over NMT for Closely Related Languages: A Case Study of Persian-Hindi Pair](https://mlsanity.com/paper/2412.16877/) (2024)

## Abstract (arXiv)

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.