Abstract
In this paper, we present phoneme level Siamese convolutional networks for the task of pair-wise cognate identification. We represent a word as a two-dimensional matrix and employ a siamese convolutional network for learning deep representations. We present siamese architectures that jointly learn phoneme level feature representations and language relatedness from raw words for cognate identification. Compared to previous works, we train and test on larger and realistic datasets; and, show that siamese architectures consistently perform better than traditional linear classifier approach.- Anthology ID:
- C16-1097
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 1018–1027
- Language:
- URL:
- https://aclanthology.org/C16-1097
- DOI:
- Cite (ACL):
- Taraka Rama. 2016. Siamese Convolutional Networks for Cognate Identification. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1018–1027, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Siamese Convolutional Networks for Cognate Identification (Rama, COLING 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/C16-1097.pdf