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    <name type="personal">
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    <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.</abstract>
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%0 Conference Proceedings
%T Convolution-Enhanced Bilingual Recursive Neural Network for Bilingual Semantic Modeling
%A Su, Jinsong
%A Zhang, Biao
%A Xiong, Deyi
%A Li, Ruochen
%A Yin, Jianmin
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F su-etal-2016-convolution
%X 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.
%U https://aclanthology.org/C16-1289
%P 3071-3081

Markdown (Informal)

[Convolution-Enhanced Bilingual Recursive Neural Network for Bilingual Semantic Modeling](https://aclanthology.org/C16-1289) (Su et al., COLING 2016)

• Convolution-Enhanced Bilingual Recursive Neural Network for Bilingual Semantic Modeling (Su et al., COLING 2016)

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.