Abstract
Estimating similarities at different levels of linguistic units, such as words, sub-phrases and phrases, is helpful for measuring semantic similarity of an entire bilingual phrase. In this paper, we propose a convolution-enhanced bilingual recursive neural network (ConvBRNN), which not only exploits word alignments to guide the generation of phrase structures but also integrates multiple-level information of the generated phrase structures into bilingual semantic modeling. In order to accurately learn the semantic hierarchy of a bilingual phrase, we develop a recursive neural network to constrain the learned bilingual phrase structures to be consistent with word alignments. Upon the generated source and target phrase structures, we stack a convolutional neural network to integrate vector representations of linguistic units on the structures into bilingual phrase embeddings. After that, we fully incorporate information of different linguistic units into a bilinear semantic similarity model. We introduce two max-margin losses to train the ConvBRNN model: one for the phrase structure inference and the other for the semantic similarity model. Experiments on NIST Chinese-English translation tasks demonstrate the high quality of the generated bilingual phrase structures with respect to word alignments and the effectiveness of learned semantic similarities on machine translation.- Anthology ID:
- C16-1289
- 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:
- 3071–3081
- Language:
- URL:
- https://aclanthology.org/C16-1289
- DOI:
- Cite (ACL):
- Jinsong Su, Biao Zhang, Deyi Xiong, Ruochen Li, and Jianmin Yin. 2016. Convolution-Enhanced Bilingual Recursive Neural Network for Bilingual Semantic Modeling. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3071–3081, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Convolution-Enhanced Bilingual Recursive Neural Network for Bilingual Semantic Modeling (Su et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/C16-1289.pdf