1 |
KAJIWARA T, MIURA B, ARASE Y. Monolingual transfer learning via bilingual translators for style-sensitive paraphrase generation[C]// Proceedings of the 34th AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2020: 8042-8049. 10.1609/aaai.v34i05.6314
|
2 |
SUTSKEVER I, VINYALS O, LE Q V. Sequence to sequence learning with neural networks[C]// Proceedings of the 27th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2014: 3104-3112.
|
3 |
宁丹丹,陈惠鹏,秦兵. 基于序列到序列模型的句子级复述生成[J]. 智能计算机与应用, 2018, 8(3):61-63, 69. 10.3969/j.issn.2095-2163.2018.03.015
|
|
NING D D, CHEN H P, QIN B. Sentence-level paraphrase generation based on sequence-to-sequence model[J]. Intelligent Computer and Applications, 2018, 8(3):61-63, 69. 10.3969/j.issn.2095-2163.2018.03.015
|
4 |
PRAKASH A, HASAN S A, LEE K, et al. Neural paraphrase generation with stacked residual LSTM networks[C]// Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers. [S.l.]: The COLING 2016 Organizing Committee, 2016: 2923-2934.
|
5 |
GUPTA A, AGARWAL A, SINGH P, et al. A deep generative framework for paraphrase generation[C]// Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2018: 5149-5156. 10.1609/aaai.v32i1.11956
|
6 |
CHI X Q, XIANG Y. Augmenting paraphrase generation with syntax information using graph convolutional networks[J]. Entropy, 2021, 23(5): No.566. 10.3390/e23050566
|
7 |
LI Y J, TARLOW D, BROCKSCHMIDT M, et al. Gated graph sequence neural networks[EB/OL]. [2021-08-17]..
|
8 |
LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: common objects in context[C]// Proceedings of the 2014 European Conference on Computer Vision, LNCS 8693. Cham: Springer, 2014: 740-755.
|
9 |
李雪晴,王石,王朱君,等. 自然语言生成综述[J]. 计算机应用, 2021, 41(5): 1227-1235. 10.11772/j.issn.1001-9081.2020071069
|
|
LI X Q, WANG S, WANG Z J, et al. Summarization of natural language generation[J]. Journal of Computer Applications, 2021, 41(5): 1227-1235. 10.11772/j.issn.1001-9081.2020071069
|
10 |
LI Z C, JIANG X, SHANG L F, et al. Decomposable neural paraphrase generation[C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: ACL, 2019: 3403-3414. 10.18653/v1/p19-1332
|
11 |
LIN Z B, LI Z R, DING N, et al. Integrating linguistic knowledge to sentence paraphrase generation[C]// Proceedings of the 34th AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2020: 8368-8375. 10.1609/aaai.v34i05.6354
|
12 |
LIN Z, WAN X J. Pushing paraphrase away from original sentence: a multi-round paraphrase generation approach[C]// Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Stroudsburg, PA: ACL, 2021: 1548-1557. 10.18653/v1/2021.findings-acl.135
|
13 |
KUMAR A, AHUJA K, VADAPALLI R, et al. Syntax-guided controlled generation of paraphrases[J]. Transactions of the Association for Computational Linguistics, 2020, 8: 330-345. 10.1162/tacl_a_00318
|
14 |
POPOVA M, SHVETS M, OLIVA J, et al. MolecularRNN: generating realistic molecular graphs with optimized properties[EB/OL]. [2021-09-05]..
|
15 |
YOU J X, YING R, REN X, et al. GraphRNN: generating realistic graphs with deep auto-regressive models[C]// Proceedings of the 35th International Conference on Machine Learning. New York: JMLR.org, 2018: 5708-5717.
|
16 |
ANAND N, HUANG P S. Generative modeling for protein structures[C]// Proceedings of the 32nd International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2018: 7505-7516.
|
17 |
FAN S F, HUANG B. Labeled graph generative adversarial networks[EB/OL]. [2021-09-28]..
|
18 |
陈雨龙,付乾坤,张岳. 图神经网络在自然语言处理中的应用[J]. 中文信息学报, 2021, 35(3): 1-23. 10.3969/j.issn.1003-0077.2021.03.001
|
|
CHEN Y L, FU Q K, ZHANG Y. Applications of graph neural network for natural language processing[J]. Journal of Chinese Information Processing, 2021, 35(3): 1-23. 10.3969/j.issn.1003-0077.2021.03.001
|
19 |
SONG L F, WANG Z G, YU M, et al. Exploring graph-structured passage representation for multi-hop reading comprehension with graph neural networks[EB/OL]. [2021-10-25].. 10.1109/tkde.2020.2982894
|
20 |
GUO X J, ZHAO L. A systematic survey on deep generative models for graph generation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022(Early Access): 1-20. 10.1109/tpami.2022.3214832
|
21 |
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, PA: ACL, 2014: 1532-1543. 10.3115/v1/d14-1162
|
22 |
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, Volume 1 (Long and Short Papers). Stroudsburg, PA: ACL, 2019: 4171-4186. 10.18653/v1/n18-2
|
23 |
SOCHER R, BAUER J, MANNING C D, et al. Parsing with compositional vector grammars[C]// Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA: ACL, 2013: 455-465.
|
24 |
CHO K, van MERRIËNBOER B, GU̇LÇEHRE Ç, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: ACL, 2014: 1724-1734. 10.3115/v1/d14-1179
|
25 |
TU Z P, LU Z D, LIU Y, et al. Modeling coverage for neural machine translation[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA: ACL, 2016: 76-85. 10.18653/v1/p16-1008
|