@inproceedings{fb7e52d8ccbc45daa3f3d260779419d8,
title = "Answer selection in community question answering by normalizing support answers",
abstract = "Answer selection in community question answering (cQA) is a common task in natural language processing. Recent progress focuses on not only pure question-answer (QA) match but also support answers [4]. In this paper, we argue that the performance can drop dramatically if noisy support answers are selected. To tackle the above issue, we propose a novel way to leverage the contributions of support answers: the match scores which are firstly normalized by the correlations between the question and the corresponding similar questions, such that the negative effect from the noisy answers can be reduced. The model applies word-to-word attention to improve QA match and employs cosine similarity as the normalization factor for support answers. Compared with previous work, experiments on the Yahoo! Answers L4 dataset show that our model achieves superior P@1 and MRR results.",
keywords = "Answer selection, Attention, Normalization, Support answer",
author = "Zhihui Zheng and Daohe Lu and Qingcai Chen and Haijun Yang and Yang Xiang and Youcheng Pan and Wei Zhong",
note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG.; 6th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2017 ; Conference date: 08-11-2017 Through 12-11-2017",
year = "2018",
doi = "10.1007/978-3-319-73618-1\_57",
language = "英语",
isbn = "9783319736174",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "672--682",
editor = "Xuanjing Huang and Jing Jiang and Dongyan Zhao and Yansong Feng and Yu Hong",
booktitle = "Natural Language Processing and Chinese Computing - 6th CCF International Conference, NLPCC 2017, Proceedings",
address = "德国",
}