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.
2021, Vol. 35 
