@inproceedings{43b8bef0b7224b508d1d63493ba3d720,
title = "ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity",
abstract = "To model semantic similarity for multilingual and cross-lingual sentence pairs, we first translate foreign languages into English, and then build an efficient monolingual English system with multiple NLP features. Our system is further supported by deep learning models and our best run achieves the mean Pearson correlation 73.16\% in primary track.",
author = "Junfeng Tian and Zhiheng Zhou and Man Lan and Yuanbin Wu",
note = "Publisher Copyright: {\textcopyright} 2017 Association for Computational Linguistics; 11th International Workshop on Semantic Evaluations, SemEval 2017, co-located with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 ; Conference date: 03-08-2017 Through 04-08-2017",
year = "2017",
doi = "10.18653/v1/S17-2028",
language = "英语",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "191--197",
booktitle = "ACL 2017 - 11th International Workshop on Semantic Evaluations, SemEval 2017, Proceedings of the Workshop",
address = "澳大利亚",
}