Comparing Recurrent and Convolutional Architectures for English-Hindi Neural Machine Translation
Sandhya Singh, Ritesh Panjwani, Anoop Kunchukuttan, Pushpak Bhattacharyya
Abstract
In this paper, we empirically compare the two encoder-decoder neural machine translation architectures: convolutional sequence to sequence model (ConvS2S) and recurrent sequence to sequence model (RNNS2S) for English-Hindi language pair as part of IIT Bombay’s submission to WAT2017 shared task. We report the results for both English-Hindi and Hindi-English direction of language pair.- Anthology ID:
- W17-5717
- Volume:
- Proceedings of the 4th Workshop on Asian Translation (WAT2017)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Toshiaki Nakazawa, Isao Goto
- Venue:
- WAT
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 167–170
- Language:
- URL:
- https://aclanthology.org/W17-5717
- DOI:
- Cite (ACL):
- Sandhya Singh, Ritesh Panjwani, Anoop Kunchukuttan, and Pushpak Bhattacharyya. 2017. Comparing Recurrent and Convolutional Architectures for English-Hindi Neural Machine Translation. In Proceedings of the 4th Workshop on Asian Translation (WAT2017), pages 167–170, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- Comparing Recurrent and Convolutional Architectures for English-Hindi Neural Machine Translation (Singh et al., WAT 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W17-5717.pdf