Abstract
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.- Anthology ID:
- N18-4016
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana, USA
- Editors:
- Silvio Ricardo Cordeiro, Shereen Oraby, Umashanthi Pavalanathan, Kyeongmin Rim
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 112–119
- Language:
- URL:
- https://aclanthology.org/N18-4016
- DOI:
- 10.18653/v1/N18-4016
- Cite (ACL):
- Sree Harsha Ramesh and Krishna Prasad Sankaranarayanan. 2018. Neural Machine Translation for Low Resource Languages using Bilingual Lexicon Induced from Comparable Corpora. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 112–119, New Orleans, Louisiana, USA. Association for Computational Linguistics.
- Cite (Informal):
- Neural Machine Translation for Low Resource Languages using Bilingual Lexicon Induced from Comparable Corpora (Ramesh & Sankaranarayanan, NAACL 2018)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/N18-4016.pdf