1 |
TAN M, SANTOS C DOS, XIANG B, et al. LSTM-based deep learning models for non-factoid answer selection [EB/OL]. (2016-03-28) [2019-01-10]. . 10.18653/v1/p16-1044
|
2 |
HE H, GIMPEL K, LIN J. Multi-perspective sentence similarity modeling with convolutional neural networks [C]// Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2015: 1576-1586. 10.18653/v1/d15-1181
|
3 |
GARG S, VU T, MOSCHITTII A. TANDA: transfer and adapt pre-trained transformer models for answer sentence selection [EB/OL]. [2020-05-01]. . 10.1609/aaai.v34i05.6282
|
4 |
孙源,王健,张益嘉,等.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. 10.3969/j.issn.1003-0077.2021.04.014
|
|
SUN Y, WANG J, ZHANG Y J, et al. Long answer selection neural model integrating coarse and fine granularity information [J]. Journal of Chinese Information Processing, 2021, 35(4): 100-109. 10.3969/j.issn.1003-0077.2021.04.014
|
5 |
冯文政,唐杰.融合深度匹配特征的答案选择模型[J].中文信息学报,2019,33(1):118-124. 10.3969/j.issn.1003-0077.2019.01.014
|
|
FENG W Z, TANG J. Answer selection model integrating depth matching features [J]. Journal of Chinese Information Processing, 2019, 33(1): 118-124. 10.3969/j.issn.1003-0077.2019.01.014
|
6 |
PETERS M E, NEUMANN M, IYYER M, et al. Deep contextualized word representations [C]// Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume1(Long Papers). Stroudsburg: ACL, 2018: 2227-2237.
|
7 |
KENTER T, BORISOV A, DE RIJKE M. Siamese CBOW: optimizing word embeddings for sentence representations [C]// Proceedings of the 2016 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg: ACL, 2016:941-951. 10.18653/v1/p16-1089
|
8 |
MUELLER J, THYAGARAJAN A. Siamese recurrent architectures for learning sentence similarity [C]// Proceedings of the 2016 30th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2016: 2786-2792. 10.1609/aaai.v34i10.7136
|
9 |
NECULOIU P, VERSTEEGH M, ROTARU M. Learning text similarity with Siamese recurrent networks [C]// Proceedings of the 1st Workshop on Representation Learning for NLP. Stroudsburg: ACL, 2016: 148-157. 10.18653/v1/w16-1617
|
10 |
BIAN W J, LI S, YANG Z, et al. A compare-aggregate model with dynamic-clip attention for answer selection [C]// Proceedings of the 2017 ACM Conference on Information and Knowledge Management. New York: ACM, 2017: 1987-1990. 10.1145/3132847.3133089
|
11 |
SHA L, ZHNAG X D, QIAN F, et al. A multi-view fusion neural network for answer selection [C]// Proceedings of the 2018 32nd AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2018: 5422-5429.
|
12 |
YOON S, DERNONCOURT F, KIM D S, et al. A compare-aggregate model with latent clustering for answer selection [C]// Proceedings of the 2019 28th ACM International Conference on Information and Knowledge Management. New York: ACM, 2019: 2093-2096. 10.1145/3357384.3358148
|
13 |
WANG S H, JIANG J. A compare-aggregate model for matching text sequences [EB/OL]. (2016-11-06) [2019-05-05]. . 10.1109/ijcnn.2019.8852062
|
14 |
TAN M, SANTOS C DOS, XIANG B, et al. Improved representation learning for question answer matching [C]// Proceedings of the 2016 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg: ACL, 2016:464-473. 10.18653/v1/p16-1044
|
15 |
SANTOS C DOS, TAN M, XIANG B, et al. Attentive pooling networks [EB/OL]. (2016-02-11) [2019-07-05]. .
|
16 |
LASKAR M T R, HUANG J, HOQUE E. Contextualized embeddings based transformer encoder for sentence similarity modeling in answer selection task [C]// Proceedings of the 2020 12th Language Resources and Evaluation Conference. Paris: European Language Resources Association, 2020: 5505-5514.
|
17 |
PENNINGTON J, SOCHER R, MANNING C D. GloVe: global vectors for word representation [C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2014: 1532-1543. 10.3115/v1/d14-1162
|
18 |
WANG M Q, SMITH N A, MITAMURA T. What is the Jeopardy model? a quasi-synchronous grammar for QA [C]// Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Stroudsburg: ACL, 2007: 22-32.
|
19 |
YANG Y, YIH W T, MEEK C. WikiQA: a challenge dataset for open-domain question answering [C]// Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2015: 2013-2018. 10.18653/v1/d15-1237
|
20 |
RAO J F, HE H, LIN J. Noise-contrastive estimation for answer selection with deep neural networks [C]// Proceedings of the 2016 25th ACM International on Conference on Information and Knowledge Management. New York: ACM, 2016: 1913-1916. 10.1145/2983323.2983872
|
21 |
TAY Y, TUAN L A, HUI S C. Multi-cast attention networks for retrieval-based question answering and response prediction [C]// Proceedings of the 2018 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2018: 2299-2308. 10.1145/3219819.3220048
|
22 |
SEVERYN A, MOSCHITTI A. Modeling relational information in question-answer pairs with convolutional neural networks [EB/OL]. (2016-04-05) [2019-08-12]. . 10.1145/2766462.2767738
|
23 |
HE H, LIN J. Pairwise word interaction modeling with deep neural networks for semantic similarity measurement [C]// Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: ACL, 2016: 937-948. 10.18653/v1/n16-1108
|
24 |
JIN Z X, ZHANG B W, ZHOU F, et al. Ranking via partial ordering for answer selection [J]. Information Sciences, 2020, 538: 358-371. 10.1016/j.ins.2020.05.110
|
25 |
DEVLIN J, CHANG M W, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding [C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume1(Long and Short Papers). Stroudsburg: ACL, 2016: 4171-4186.
|
26 |
HOWARD J, RUDER S. Universal language model fine-tuning for text classification [C]// Proceedings of the 2018 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg: ACL, 2018: 328-339. 10.18653/v1/p18-1031
|