# Neural Machine Translation for Low Resource Languages using Bilingual Lexicon Induced from Comparable Corpora

arXiv: https://arxiv.org/abs/1806.09652 · HTML: https://mlsanity.com/paper/1806.09652/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [LexC-Gen: Generating Data for Extremely Low-Resource Languages with Large Language Models and Bilingual Lexicons](https://mlsanity.com/paper/2402.14086/) (2024)
- [Majority Voting with Bidirectional Pre-translation For Bitext Retrieval](https://mlsanity.com/paper/2103.06369/) (2021)
- [HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints](https://mlsanity.com/paper/2109.04443/) (2021)
- [Building the Language Resource for a Cebuano-Filipino Neural Machine Translation System](https://mlsanity.com/paper/2110.15716/) (2021)
- [Finetuning a Kalaallisut-English machine translation system using web-crawled data](https://mlsanity.com/paper/2206.02230/) (2022)

## Abstract (arXiv)

Resources for the non-English languages are scarce and this paper addresses this problem in the context of machine translation, by automatically extracting parallel sentence pairs from the multilingual articles available on the Internet. In this paper, we have used an end-to-end Siamese bidirectional recurrent neural network to generate parallel sentences from comparable multilingual articles in Wikipedia. Subsequently, we have showed that using the harvested dataset improved BLEU scores on both NMT and phrase-based SMT systems for the low-resource language pairs: English--Hindi and English--Tamil, when compared to training exclusively on the limited bilingual corpora collected for these language pairs.