@InProceedings{wang-EtAl:2018:C18-17,
  author    = {Wang, Lu  and  Li, Shoushan  and  Sun, Changlong  and  Si, Luo  and  Liu, Xiaozhong  and  Zhang, Min  and  Zhou, Guodong},
  title     = {One vs. Many QA Matching with both Word-level and Sentence-level Attention Network},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {2540--2550},
  abstract  = {Question-Answer (QA) matching is a fundamental task in the Natural Language Processing community. In this paper, we first build a novel QA matching corpus with informal text which is collected from a product reviewing website. Then, we propose a novel QA matching approach, namely One vs. Many Matching, which aims to address the novel scenario where one question sentence often has an answer with multiple sentences. Furthermore, we improve our matching approach by employing both word-level and sentence-level attentions for solving the noisy problem in the informal text. Empirical studies demonstrate the effectiveness of the proposed approach to question-answer matching.},
  url       = {http://www.aclweb.org/anthology/C18-1215}
}