@inproceedings{tian-etal-2017-ecnu,
title = "{ECNU} at {S}em{E}val-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",
author = "Tian, Junfeng and
Zhou, Zhiheng and
Lan, Man and
Wu, Yuanbin",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2028/",
doi = "10.18653/v1/S17-2028",
pages = "191--197",
abstract = "To address semantic similarity on multilingual and cross-lingual sentences, we firstly translate other foreign languages into English, and then feed our monolingual English system with various interactive features. Our system is further supported by combining with deep learning semantic similarity and our best run achieves the mean Pearson correlation 73.16\% in primary track."
}
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<abstract>To address semantic similarity on multilingual and cross-lingual sentences, we firstly translate other foreign languages into English, and then feed our monolingual English system with various interactive features. Our system is further supported by combining with deep learning semantic similarity and our best run achieves the mean Pearson correlation 73.16% in primary track.</abstract>
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%0 Conference Proceedings
%T 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
%A Tian, Junfeng
%A Zhou, Zhiheng
%A Lan, Man
%A Wu, Yuanbin
%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 tian-etal-2017-ecnu
%X To address semantic similarity on multilingual and cross-lingual sentences, we firstly translate other foreign languages into English, and then feed our monolingual English system with various interactive features. Our system is further supported by combining with deep learning semantic similarity and our best run achieves the mean Pearson correlation 73.16% in primary track.
%R 10.18653/v1/S17-2028
%U https://aclanthology.org/S17-2028/
%U https://doi.org/10.18653/v1/S17-2028
%P 191-197
Markdown (Informal)
[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](https://aclanthology.org/S17-2028/) (Tian et al., SemEval 2017)
ACL