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        <title>HCTI at SemEval-2017 Task 1: Use convolutional neural network to evaluate Semantic Textual Similarity</title>
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        <namePart type="given">Yang</namePart>
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            <namePart type="given">Steven</namePart>
            <namePart type="family">Bethard</namePart>
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            <namePart type="given">Marianna</namePart>
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            <namePart type="given">Saif</namePart>
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    <abstract>This paper describes our convolutional neural network (CNN) system for Semantic Textual Similarity (STS) task. We calculated semantic similarity score between two sentences by comparing their semantic vectors. We generated semantic vector of every sentence by max pooling every dimension of their word vectors. There are mainly two trick points in our system. One is that we trained a CNN to transfer GloVe word vectors to a more proper form for STS task before pooling. Another is that we trained a fully-connected neural network (FCNN) to transfer difference of two semantic vectors to probability of every similarity score. We decided all hyper parameters empirically. In spite of the simplicity of our neural network system, we achieved a good accuracy and ranked 3rd in primary track of SemEval 2017.</abstract>
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%0 Conference Proceedings
%T HCTI at SemEval-2017 Task 1: Use convolutional neural network to evaluate Semantic Textual Similarity
%A Shao, Yang
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F shao-2017-hcti
%X This paper describes our convolutional neural network (CNN) system for Semantic Textual Similarity (STS) task. We calculated semantic similarity score between two sentences by comparing their semantic vectors. We generated semantic vector of every sentence by max pooling every dimension of their word vectors. There are mainly two trick points in our system. One is that we trained a CNN to transfer GloVe word vectors to a more proper form for STS task before pooling. Another is that we trained a fully-connected neural network (FCNN) to transfer difference of two semantic vectors to probability of every similarity score. We decided all hyper parameters empirically. In spite of the simplicity of our neural network system, we achieved a good accuracy and ranked 3rd in primary track of SemEval 2017.
%R 10.18653/v1/S17-2016
%U https://aclanthology.org/S17-2016
%U https://doi.org/10.18653/v1/S17-2016
%P 130-133

Markdown (Informal)

[HCTI at SemEval-2017 Task 1: Use convolutional neural network to evaluate Semantic Textual Similarity](https://aclanthology.org/S17-2016) (Shao, SemEval 2017)

• HCTI at SemEval-2017 Task 1: Use convolutional neural network to evaluate Semantic Textual Similarity (Shao, SemEval 2017)

ACL

• Yang Shao. 2017. HCTI at SemEval-2017 Task 1: Use convolutional neural network to evaluate Semantic Textual Similarity. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 130–133, Vancouver, Canada. Association for Computational Linguistics.