Generalizing from a Few Examples A Survey on Few-Shot Learning

paper： https://arxiv.org/abs/1904.05046

git: https://github.com/tata1661/FSL-Mate/tree/master/FewShotPapers#Applications

原文按应用对FSL做了总结，与NLP相关的有：

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14. Self-supervised meta-learning for few-shot natural language classification tasks, in EMNLP, 2020. T. Bansal, R. Jha, T. Munkhdalai, and A. McCallum. paper code
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26. SALNet: Semi-supervised few-shot text classification with attention-based lexicon construction, in AAAI, 2021. J.-H. Lee, S.-K. Ko, and Y.-S. Han. paper
27. Learning from my friends: Few-shot personalized conversation systems via social networks, in AAAI, 2021. Z. Tian, W. Bi, Z. Zhang, D. Lee, Y. Song, and N. L. Zhang. paper code
28. Relative and absolute location embedding for few-shot node classification on graph, in AAAI, 2021. Z. Liu, Y. Fang, C. Liu, and S. C.H. Hoi. paper
29. Few-shot question answering by pretraining span selection, in ACL-IJCNLP, 2021. O. Ram, Y. Kirstain, J. Berant, A. Globerson, and O. Levy. paper code
30. A closer look at few-shot crosslingual transfer: The choice of shots matters, in ACL-IJCNLP, 2021. M. Zhao, Y. Zhu, E. Shareghi, I. Vulic, R. Reichart, A. Korhonen, and H. Schütze. paper code
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32. Distinct label representations for few-shot text classification, in ACL-IJCNLP, 2021. S. Ohashi, J. Takayama, T. Kajiwara, and Y. Arase. paper code
33. Entity concept-enhanced few-shot relation extraction, in ACL-IJCNLP, 2021. S. Yang, Y. Zhang, G. Niu, Q. Zhao, and S. Pu. paper code
34. On training instance selection for few-shot neural text generation, in ACL-IJCNLP, 2021. E. Chang, X. Shen, H.-S. Yeh, and V. Demberg. paper code
35. Unsupervised neural machine translation for low-resource domains via meta-learning, in ACL-IJCNLP, 2021. C. Park, Y. Tae, T. Kim, S. Yang, M. A. Khan, L. Park, and J. Choo. paper code
36. Meta-learning with variational semantic memory for word sense disambiguation, in ACL-IJCNLP, 2021. Y. Du, N. Holla, X. Zhen, C. Snoek, and E. Shutova. paper code
37. Multi-label few-shot learning for aspect category detection, in ACL-IJCNLP, 2021. M. Hu, S. Z. H. Guo, C. Xue, H. Gao, T. Gao, R. Cheng, and Z. Su. paper
38. TextSETTR: Few-shot text style extraction and tunable targeted restyling, in ACL-IJCNLP, 2021. P. Rileya, N. Constantb, M. Guob, G. Kumarc, D. Uthusb, and Z. Parekh. paper
39. Few-shot text ranking with meta adapted synthetic weak supervision, in ACL-IJCNLP, 2021. S. Sun, Y. Qian, Z. Liu, C. Xiong, K. Zhang, J. Bao, Z. Liu, and P. Bennett. paper code
40. PROTAUGMENT: Intent detection meta-learning through unsupervised diverse paraphrasing, in ACL-IJCNLP, 2021. T. Dopierre, C. Gravier, and W. Logerais. paper code
41. AUGNLG: Few-shot natural language generation using self-trained data augmentation, in ACL-IJCNLP, 2021. X. Xu, G. Wang, Y.-B. Kim, and S. Lee. paper code
42. Meta self-training for few-shot neural sequence labeling, in KDD, 2021. Y. Wang, S. Mukherjee, H. Chu, Y. Tu, M. Wu, J. Gao, and A. H. Awadallah. paper code
43. Knowledge-enhanced domain adaptation in few-shot relation classification, in KDD, 2021. J. Zhang, J. Zhu, Y. Yang, W. Shi, C. Zhang, and H. Wang. paper code
44. Few-shot text classification with triplet networks, data augmentation, and curriculum learning, in NAACL-HLT, 2021. J. Wei, C. Huang, S. Vosoughi, Y. Cheng, and S. Xu. paper code
45. Few-shot intent classification and slot filling with retrieved examples, in NAACL-HLT, 2021. D. Yu, L. He, Y. Zhang, X. Du, P. Pasupat, and Q. Li. paper
46. Non-parametric few-shot learning for word sense disambiguation, in NAACL-HLT, 2021. H. Chen, M. Xia, and D. Chen. paper code
47. Towards few-shot fact-checking via perplexity, in NAACL-HLT, 2021. N. Lee, Y. Bang, A. Madotto, and P. Fung. paper
48. ConVEx: Data-efficient and few-shot slot labeling, in NAACL-HLT, 2021. M. Henderson, and I. Vulic. paper
49. Few-shot text generation with natural language instructions, in EMNLP, 2021. T. Schick, and H. Schütze. paper
50. Towards realistic few-shot relation extraction, in EMNLP, 2021. S. Brody, S. Wu, and A. Benton. paper code
51. Few-shot emotion recognition in conversation with sequential prototypical networks, in EMNLP, 2021. G. Guibon, M. Labeau, H. Flamein, L. Lefeuvre, and C. Clavel. paper code
52. Learning prototype representations across few-shot tasks for event detection, in EMNLP, 2021. V. Lai, F. Dernoncourt, and T. H. Nguyen. paper
53. Exploring task difficulty for few-shot relation extraction, in EMNLP, 2021. J. Han, B. Cheng, and W. Lu. paper code
54. Honey or poison? Solving the trigger curse in few-shot event detection via causal intervention, in EMNLP, 2021. J. Chen, H. Lin, X. Han, and L. Sun. paper code
55. Nearest neighbour few-shot learning for cross-lingual classification, in EMNLP, 2021. M. S. Bari, B. Haider, and S. Mansour. paper
56. Knowledge-aware meta-learning for low-resource text classification, in EMNLP, 2021. H. Yao, Y. Wu, M. Al-Shedivat, and E. P. Xing. paper code
57. Few-shot named entity recognition: An empirical baseline study, in EMNLP, 2021. J. Huang, C. Li, K. Subudhi, D. Jose, S. Balakrishnan, W. Chen, B. Peng, J. Gao, and J. Han. paper
58. MetaTS: Meta teacher-student network for multilingual sequence labeling with minimal supervision, in EMNLP, 2021. Z. Li, D. Zhang, T. Cao, Y. Wei, Y. Song, and B. Yin. paper
59. Meta-LMTC: Meta-learning for large-scale multi-label text classification, in EMNLP, 2021. R. Wang, X. Su, S. Long, X. Dai, S. Huang, and J. Chen. paper