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Cross-Lingual Semantic Textual Similarity Modeling Using Neural Networks

  • Xia LiEmail author
  • Minping Chen
  • Zihang Zeng
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 954)

Abstract

Cross-lingual semantic textual similarity is to measure the semantic similarity of sentences in different languages. Previous work pay more attention on leveraging traditional NLP features (e.g., alignment features, syntactic features) to evaluate the semantic similarity of sentences. In this paper, we only use word embedding as basic features without any handcrafted features and build a model which is able to capture local and global semantic information of the sentences to evaluate semantic textual similarity. We test our model on SemEval-2017 and STS benchmark datasets. Our experiments show that our model improves the performance of the semantic textual similarity and achieves the best results compared with the baseline neural-network based methods reported on the two datasets.

Keywords

Cross-lingual semantic textual similarity SemEval-2017 Neural networks 

Notes

Acknowledgement

This work is supported by the National Science Foundation of China (61402119) and Special Funds for the Cultivation of Guangdong College Students’ Scientific and Technological Innovation. (“Climbing Program” Special Funds.)

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Key Laboratory of Language Engineering and ComputingGuangdong University of Foreign StudiesGuangzhouChina
  2. 2.School of Information Science and Technology/School of Cyber SecurityGuangdong University of Foreign StudiesGuangzhouChina

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