@inproceedings{zhang-etal-2017-furongwang,
title = "{F}u{R}ong{W}ang at {S}em{E}val-2017 Task 3: Deep Neural Networks for Selecting Relevant Answers in Community Question Answering",
author = "Zhang, Sheng and
Cheng, Jiajun and
Wang, Hui and
Zhang, Xin and
Li, Pei and
Ding, Zhaoyun",
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/S17-2052",
doi = "10.18653/v1/S17-2052",
pages = "320--325",
abstract = "We describes deep neural networks frameworks in this paper to address the community question answering (cQA) ranking task (SemEval-2017 task 3). Convolutional neural networks and bi-directional long-short term memory networks are applied in our methods to extract semantic information from questions and answers (comments). In addition, in order to take the full advantage of question-comment semantic relevance, we deploy interaction layer and augmented features before calculating the similarity. The results show that our methods have the great effectiveness for both subtask A and subtask C.",
}
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%0 Conference Proceedings
%T FuRongWang at SemEval-2017 Task 3: Deep Neural Networks for Selecting Relevant Answers in Community Question Answering
%A Zhang, Sheng
%A Cheng, Jiajun
%A Wang, Hui
%A Zhang, Xin
%A Li, Pei
%A Ding, Zhaoyun
%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 zhang-etal-2017-furongwang
%X We describes deep neural networks frameworks in this paper to address the community question answering (cQA) ranking task (SemEval-2017 task 3). Convolutional neural networks and bi-directional long-short term memory networks are applied in our methods to extract semantic information from questions and answers (comments). In addition, in order to take the full advantage of question-comment semantic relevance, we deploy interaction layer and augmented features before calculating the similarity. The results show that our methods have the great effectiveness for both subtask A and subtask C.
%R 10.18653/v1/S17-2052
%U https://aclanthology.org/S17-2052
%U https://doi.org/10.18653/v1/S17-2052
%P 320-325
Markdown (Informal)
[FuRongWang at SemEval-2017 Task 3: Deep Neural Networks for Selecting Relevant Answers in Community Question Answering](https://aclanthology.org/S17-2052) (Zhang et al., SemEval 2017)
ACL