孙源,王健,张益嘉,钱凌飞,林鸿飞. 融合粗细粒度信息的长答案选择神经网络模型[J]. 中文信息学报, 2021, 35(4): 100-109.
SUN Yuan, WANG Jian, ZHANG Yijia, QIAN Lingfei, LIN Hongfei. A Neural Network for Long Answer Selection with Coarse and Fine-grained Information. , 2021, 35(4): 100-109.
融合粗细粒度信息的长答案选择神经网络模型
孙源,王健,张益嘉,钱凌飞,林鸿飞
大连理工大学 计算机科学与技术学院,辽宁 大连 116024
A Neural Network for Long Answer Selection with Coarse and Fine-grained Information
SUN Yuan, WANG Jian, ZHANG Yijia, QIAN Lingfei, LIN Hongfei
School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China
Abstract:The long answer selection plays an important role in non-factoid question answering systems such as community question answering and open-domain question answering systems. To improve the performance of long answer selection, we propose a novel model which combines coarse (sentence-level) and fine-grained (word-level) information. Our model also alleviates the following two issues: ① not all the important information in a long sequence can be modeled by a single vector, and ② the failure to capture global information under the compare-aggregate framework. Besides, our model uses fine-grained information without extra training parameters. The experiments on InsuranceQA dataset show that the proposed model outperforms the state-of-the-art sequence models by 3.30% in accuracy.
[1] Feng M, Xiang B, Glass M R, et al. Applying deep learning to answer selection: A study and an open task[C]//Proceedings of IEEE Workshop on Automatic Speech Recognition and Understanding(ASRU). Scottsdale, AR: IEEE, 2015; 813-820. [2] Ruckle A, Moosavi N S, Gurevych I. COALA: A neural coverage-based approach for long answer selection with small data[C]//Proceedings of the 9th AAAI Symposium on Educational Advances in Artificial Intelligence. Honolulu, HI: Assoc Advancement Artificial Intelligence, 2019; 6932-6939. [3] Hu B, Lu Z, Li H, et al. Convolutional neural network architectures for matching natural language sentences[C]//Proceedings of Advances in Neural Information Processing Systems. Curran Associates, Inc., 2014; 2042-2050. [4] Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate[C]//Proceedings of the 3rd International Conference on Learning Representations,2015: 1409-1473. [5] Wang B, Liu K, Zhao J. Inner attention based recurrent neural networks for answer selection[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics(ACL). Berlin, Germany: Association for Computational Linguistics, 2016; 1288-1297. [6] Yang R, Zhang J, Gao X, et al. Simple and effective text matching with richer alignment features[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics(ACL). Florence, Italy: Association for Computational Linguistics, 2019; 4699-4709. [7] Yin W, Sch U Tze H, Xiang B, et al. ABCNN: Attention-based convolutional neural network for modeling sentence pairs[J]. Transactions of the Association for Computational Linguistics. 2016, 4: 259-272. [8] Wang S, Jiang J. A compare-aggregate model for matching text sequences[C]//Proceedings of the 5th International Conference on Learning Representations(ICLR). 2017:1-15. [9] Tay Y, Luu A T, Hui S C. Hermitian co-attention networks for text matching in asymmetrical domains[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence(IJCAI). Stockholm, Sweden, 2018: 4425-4431. [10] Maia M, Handschuh S, Freitas A E, et al. WWW18 open challenge: Financial opinion mining and question answering[C]//Proceedings of WWW18. Republic and Canton of Geneva, Switzerland, 2018: 1941-1942. [11] Tran N K, Niedere E E C. Multihop attention networks for question answer matching[C]//Proceedings of SIGIR18. New York, NY, USA, 2018: 325-334. [12] Cohen D, Yang L, Croft W B. WikiPassageQA: A benchmark collection for research on non-factoid answer passage retrieval[C]//Proceedings of SIGIR18. New York, NY, USA, 2018: 1165-1168. [13] Tan M, Dos Santos C, Xiang B, et al. Improved representation learning for question answer matching[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Berlin, Germany: Association for Computational Linguistics, 2016: 464-473. [14] Bojanowski P, Grave E, Joulin A, et al. Enriching word vectors with subword information[J]. Transactions of the Association for Computational Linguistics, 2017, 5: 135-146.