Few Shot Learning Papers

Top conference papers related to few-shot learning in the past three years

会议/年份	2019	2020	2021
ACL	3	8	15
EMNLP	8	14	34
NAACL	0	-	11
COLING	-	13	-

ACL-2019

1. Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification Zhi-Xiu Ye | Zhen-Hua Ling [paper] [code]

2. Few-Shot Representation Learning for Out-Of-Vocabulary Words Ziniu Hu | Ting Chen | Kai-Wei Chang | Yizhou Sun [paper] [code]

3. Give It a Shot: Few-shot Learning to Normalize ADR Mentions in Social Media Posts Emmanouil Manousogiannis | Sepideh Mesbah | Alessandro Bozzon | Selene Baez | Robert Jan Sips [paper]

ACL-2020

1. Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations Samuel Coope | Tyler Farghly | Daniela Gerz | Ivan Vulić | Matthew Henderson [paper] [code]

2. Few-Shot NLG with Pre-Trained Language Model Zhiyu Chen | Harini Eavani | Wenhu Chen | Yinyin Liu | William Yang Wang [paper] [code]

3. Dynamic Memory Induction Networks for Few-Shot Text Classification Ruiying Geng | Binhua Li | Yongbin Li | Jian Sun | Xiaodan Zhu [paper]

4. Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network Yutai Hou | Wanxiang Che | Yongkui Lai | Zhihan Zhou | Yijia Liu | Han Liu | Ting Liu [paper] [code]

5. Shaping Visual Representations with Language for Few-Shot Classification Jesse Mu | Percy Liang | Noah Goodman [paper] [code]

6. Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks Yiping Song | Zequn Liu | Wei Bi | Rui Yan | Ming Zhang [paper] [code]

7. Meta-Learning for Few-Shot NMT Adaptation Amr Sharaf | Hany Hassan | Hal Daumé III [paper]

8. Extensively Matching for Few-shot Learning Event Detection Viet Dac Lai | Thien Huu Nguyen | Franck Dernoncourt [paper]

ACL-2021

1. AugNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation Xinnuo Xu | Guoyin Wang | Young-Bum Kim | Sungjin Lee [paper] [code]

2. Few-Shot Question Answering by Pretraining Span Selection Ori Ram | Yuval Kirstain | Jonathan Berant | Amir Globerson | Omer Levy [paper] [code]

3. Few-NERD: A Few-shot Named Entity Recognition Dataset Ning Ding | Guangwei Xu | Yulin Chen | Xiaobin Wang | Xu Han | Pengjun Xie | Haitao Zheng | Zhiyuan Liu [paper] [code]

4. TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling Parker Riley | Noah Constant | Mandy Guo | Girish Kumar | David Uthus | Zarana Parekh [paper]

5. Making Pre-trained Language Models Better Few-shot Learners Tianyu Gao | Adam Fisch | Danqi Chen [paper] [code]

6. Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision Si Sun | Yingzhuo Qian | Zhenghao Liu | Chenyan Xiong | Kaitao Zhang | Jie Bao | Zhiyuan Liu | Paul Bennett [paper] [code]

7. A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters Mengjie Zhao | Yi Zhu | Ehsan Shareghi | Ivan Vulić | Roi Reichart | Anna Korhonen | Hinrich Schütze [paper]

8. Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition Meihan Tong | Shuai Wang | Bin Xu | Yixin Cao | Minghui Liu | Lei Hou | Juanzi Li [paper]

9. Multi-Label Few-Shot Learning for Aspect Category Detection Mengting Hu | Shiwan Zhao | Honglei Guo | Chao Xue | Hang Gao | Tiegang Gao | Renhong Cheng | Zhong Su [paper]

10. On Training Instance Selection for Few-Shot Neural Text Generation Ernie Chang | Xiaoyu Shen | Hui-Syuan Yeh | Vera Demberg [paper]

11. Distinct Label Representations for Few-Shot Text Classification Sora Ohashi | Junya Takayama | Tomoyuki Kajiwara | Yuki Arase [paper] [code]

12. Entity Concept-enhanced Few-shot Relation Extraction Shan Yang | Yongfei Zhang | Guanglin Niu | Qinghua Zhao | Shiliang Pu [paper] [code]

13. Meta-Learning for Few-Shot Named Entity Recognition Cyprien de Lichy | Hadrien Glaude | William Campbell [paper]

14. Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification Yue Li | Jiong Zhang [paper]

15. UoB_UK at SemEval 2021 Task 2: Zero-Shot and Few-Shot Learning for Multi-lingual and Cross-lingual Word Sense Disambiguation. Wei Li | Harish Tayyar Madabushi | Mark Lee [paper]

EMNLP-2019

1. Hierarchical Attention Prototypical Networks for Few-Shot Text Classification Shengli Sun | Qingfeng Sun | Kevin Zhou | Tengchao Lv [paper]

2. Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations Xin Lv | Yuxian Gu | Xu Han | Lei Hou | Juanzi Li | Zhiyuan Liu [paper] [code]

3. Induction Networks for Few-Shot Text Classification Ruiying Geng | Binhua Li | Yongbin Li | Xiaodan Zhu | Ping Jian | Jian Sun [paper] [code]

4. Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs Mingyang Chen | Wen Zhang | Wei Zhang | Qiang Chen | Huajun Chen [paper] [code]

5. FewRel 2.0: Towards More Challenging Few-Shot Relation Classification Tianyu Gao | Xu Han | Hao Zhu | Zhiyuan Liu | Peng Li | Maosong Sun | Jie Zhou [paper] [code]

6. A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification Varun Kumar | Hadrien Glaude | Cyprien de Lichy | Wlliam Campbell [paper]

7. Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models Jeroen Van Hautte | Guy Emerson | Marek Rei [paper]

8. Few-Shot and Zero-Shot Learning for Historical Text Normalization Marcel Bollmann | Natalia Korchagina | Anders Søgaard [paper]

EMNLP-2020

1. Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks Trapit Bansal | Rishikesh Jha | Tsendsuren Munkhdalai | Andrew McCallum [paper] [code]

2. Adaptive Attentional Network for Few-Shot Knowledge Graph Completion Jiawei Sheng | Shu Guo | Zhenyu Chen | Juwei Yue | Lihong Wang | Tingwen Liu | Hongbo Xu [paper] [code]

3. Few-Shot Learning for Opinion Summarization Arthur Bražinskas | Mirella Lapata | Ivan Titov [paper] [code]

4. Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models Ethan Wilcox | Peng Qian | Richard Futrell | Ryosuke Kohita | Roger Levy | Miguel Ballesteros [paper]

5. Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference Jianguo Zhang | Kazuma Hashimoto | Wenhao Liu | Chien-Sheng Wu | Yao Wan | Philip Yu | Richard Socher | Caiming Xiong [paper] [code]

6. Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning Yuncheng Hua | Yuan-Fang Li | Gholamreza Haffari | Guilin Qi | Tongtong Wu [paper] [code]

7. Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning Yi Yang | Arzoo Katiyar [paper] [code]

8. Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start Wenpeng Yin | Nazneen Fatema Rajani | Dragomir Radev | Richard Socher | Caiming Xiong [paper] [code]

9. Few-shot Natural Language Generation for Task-Oriented Dialog Baolin Peng | Chenguang Zhu | Chunyuan Li | Xiujun Li | Jinchao Li | Michael Zeng | Jianfeng Gao [paper] [code]

10. Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases Chuxu Zhang | Lu Yu | Mandana Saebi | Meng Jiang | Nitesh Chawla [paper]

11. Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection Hoang Nguyen | Chenwei Zhang | Congying Xia | Philip Yu [paper] [code]

12. Composed Variational Natural Language Generation for Few-shot Intents Congying Xia | Caiming Xiong| Philip Yu | Richard Socher [paper]

13. Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines Łukasz Borchmann| Dawid Wisniewski | Andrzej Gretkowski | Izabela Kosmala | Dawid Jurkiewicz | Łukasz Szałkiewicz | Gabriela Pałka | Karol Kaczmarek | Agnieszka Kaliska | Filip Graliński [paper] [code]

14. Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation Nithin Holla | Pushkar Mishra | Helen Yannakoudakis | Ekaterina Shutova [paper] [code]

EMNLP-2021

1. Few-Shot Text Generation with Natural Language Instructions Timo Schick | Hinrich Schütze [paper]

2. Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction Oscar Sainz | Oier Lopez de Lacalle | Gorka Labaka | Ander Barrena | Eneko Agirre [paper] [code]

3. Nearest Neighbour Few-Shot Learning for Cross-lingual Classification M Saiful Bari | Batool Haider | Saab Mansour [paper]

4. Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems Fei Mi | Wanhao Zhou | Lingjing Kong | Fengyu Cai | Minlie Huang | Boi Faltings [paper]

5. Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning Jianguo Zhang | Trung Bui | Seunghyun Yoon | Xiang Chen | Zhiwei Liu | Congying Xia | Quan Hung Tran | Walter Chang | Philip Yu [paper] [code]

6. Exploring Task Difficulty for Few-Shot Relation Extraction Jiale Han | Bo Cheng | Wei Lu [paper] [code]

7. TransPrompt: Towards an Automatic Transferable Prompting Framework for Few-shot Text Classification Chengyu Wang | Jianing Wang | Minghui Qiu | Jun Huang | Ming Gao [paper]

8. Learning Prototype Representations Across Few-Shot Tasks for Event Detection Viet Lai | Franck Dernoncourt | Thien Huu Nguyen [paper] [code]

9. Towards Realistic Few-Shot Relation Extraction Sam Brody | Sichao Wu | Adrian Benton [paper] [code]

10. Continual Few-Shot Learning for Text Classification Ramakanth Pasunuru | Veselin Stoyanov | Mohit Bansal [paper] [code]

11. STraTA: Self-Training with Task Augmentation for Better Few-shot Learning Tu Vu | Minh-Thang Luong | Quoc Le | Grady Simon | Mohit Iyyer [paper] [code]

12. FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models Rakesh Chada | Pradeep Natarajan [paper]

13. Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks Gaël Guibon | Matthieu Labeau | Hélène Flamein | Luce Lefeuvre | Chloé Clavel [paper] [code]

14. CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP Qinyuan Ye | Bill Yuchen Lin | Xiang Ren [paper] [code]

15. Constrained Language Models Yield Few-Shot Semantic Parsers Richard Shin | Christopher Lin | Sam Thomson | Charles Chen | Subhro Roy | Emmanouil Antonios Platanios | Adam Pauls | Dan Klein | Jason Eisner | Benjamin Van Durme [paper] [code]

16. Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention Jiawei Chen | Hongyu Lin | Xianpei Han | Le Sun [paper] [code]

17. Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning Prasetya Utama | Nafise Sadat Moosavi | Victor Sanh | Iryna Gurevych [paper] [code]

18. Revisiting Self-training for Few-shot Learning of Language Model Yiming Chen | Yan Zhang | Chen Zhang | Grandee Lee | Ran Cheng | Haizhou Li [paper] [code]

19. Language Models are Few-Shot Butlers Vincent Micheli | Francois Fleuret [paper] [code]

20. Few-Shot Named Entity Recognition: An Empirical Baseline Study Jiaxin Huang | Chunyuan Li | Krishan Subudhi | Damien Jose | Shobana Balakrishnan | Weizhu Chen | Baolin Peng | Jianfeng Gao | Jiawei Han [paper]

21. Self-training with Few-shot Rationalization Meghana Moorthy Bhat | Alessandro Sordoni | Subhabrata Mukherjee [paper]

22. P-INT: A Path-based Interaction Model for Few-shot Knowledge Graph Completion Jingwen Xu | Jing Zhang | Xirui Ke | Yuxiao Dong | Hong Chen | Cuiping Li | Yongbin Liu [paper]

23. Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning Xisen Jin | Bill Yuchen Lin | Mohammad Rostami | Xiang Ren [paper] [code]

24. Few-Shot Table-to-Text Generation with Prototype Memory Yixuan Su | Zaiqiao Meng | Simon Baker | Nigel Collier [paper] [code]

25. Effectiveness of Pre-training for Few-shot Intent Classification Haode Zhang | Yuwei Zhang | Li-Ming Zhan | Jiaxin Chen | Guangyuan Shi | Xiao-Ming Wu | Albert Y.S. Lam [paper]

26. An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling Han Liu | Feng Zhang | Xiaotong Zhang | Siyang Zhao | Xianchao Zhang [paper]

27. Few-Shot Novel Concept Learning for Semantic Parsing Soham Dan | Osbert Bastani | Dan Roth [paper]

28. Self-Training using Rules of Grammar for Few-Shot NLU Joonghyuk Hahn | Hyunjoon Cheon | Kyuyeol Han | Cheongjae Lee | Junseok Kim | Yo-Sub Han [paper]

29. Data-Efficient Language Shaped Few-shot Image Classification Zhenwen Liang | Xiangliang Zhang [paper] [code]

30. Investigating the Effect of Natural Language Explanations on Out-of-Distribution Generalization in Few-shot NLI Yangqiaoyu Zhou | Chenhao Tan [paper] [code]

31. Language Models are Few-shot Multilingual Learners Genta Indra Winata | Andrea Madotto | Zhaojiang Lin | Rosanne Liu | Jason Yosinski | Pascale Fung [paper] [code]

32. Few-shot and Zero-shot Approaches to Legal Text Classification: A Case Study in the Financial Sector Rajdeep Sarkar | Atul Kr. Ojha | Jay Megaro | John Mariano | Vall Herard | John P. McCrae [paper]

33. Few-Shot Intent Classification by Gauging Entailment Relationship Between Utterance and Semantic Label Jin Qu | Kazuma Hashimoto | Wenhao Liu | Caiming Xiong | Yingbo Zhou [paper]

34. Towards Zero and Few-shot Knowledge-seeking Turn Detection in Task-orientated Dialogue Systems Di Jin | Shuyang Gao | Seokhwan Kim | Yang Liu | Dilek Hakkani-Tur [paper] [code]

NAACL-2019

NAACL-2020

NAACL-2021

1. Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation Alexander Fabbri | Simeng Han | Haoyuan Li | Haoran Li | Marjan Ghazvininejad | Shafiq Joty | Dragomir Radev | Yashar Mehdad [paper]

2. Few-shot Intent Classification and Slot Filling with Retrieved Examples Dian Yu | Luheng He | Yuan Zhang | Xinya Du | Panupong Pasupat | Qi Li [paper]

3. DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference Shikhar Murty | Tatsunori B. Hashimoto | Christopher Manning [paper]

4. Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System Congying Xia | Wenpeng Yin | Yihao Feng | Philip Yu [paper]

5. Non-Parametric Few-Shot Learning for Word Sense Disambiguation Howard Chen | Mengzhou Xia | Danqi Chen [paper][code]

6. Towards Few-shot Fact-Checking via Perplexity Nayeon Lee | Yejin Bang | Andrea Madotto | Pascale Fung [paper]

7. It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners Timo Schick | Hinrich Schütze [paper][code]

8. Knowledge Guided Metric Learning for Few-Shot Text Classification Dianbo Sui | Yubo Chen | Binjie Mao | Delai Qiu | Kang Liu | Jun Zhao [paper]

9. ConVEx: Data-Efficient and Few-Shot Slot Labeling Matthew Henderson | Ivan Vulić [paper]

10. Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning Jason Wei | Chengyu Huang | Soroush Vosoughi | Yu Cheng | Shiqi Xu [paper][code]

11. Scalable Few-Shot Learning of Robust Biomedical Name Representations Pieter Fivez | Simon Suster | Walter Daelemans [paper][code]

COLING-2019

COLING-2020

1. Effective Few-Shot Classification with Transfer Learning Aakriti Gupta | Kapil Thadani | Neil O’Hare [paper]

2. Meta-Information Guided Meta-Learning for Few-Shot Relation Classification Bowen Dong | Yuan Yao | Ruobing Xie | Tianyu Gao | Xu Han | Zhiyuan Liu | Fen Lin | Leyu Lin | Maosong Sun [paper][code]

3. A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification Haopeng Ren | Yi Cai | Xiaofeng Chen | Guohua Wang | Qing Li [paper]

4. TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching Heng Gong | Yawei Sun | Xiaocheng Feng | Bing Qin | Wei Bi | Xiaojiang Liu | Ting Liu [paper]

5. Emergent Communication Pretraining for Few-Shot Machine Translation Yaoyiran Li | Edoardo Maria Ponti | Ivan Vulić | Anna Korhonen [paper][code]

6. Few-shot Pseudo-Labeling for Intent Detection Thomas Dopierre | Christophe Gravier | Julien Subercaze | Wilfried Logerais [paper]

7. Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks Trapit Bansal | Rishikesh Jha | Andrew McCallum [paper][code]

8. Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network Chen Lyu | Weijie Liu | Ping Wang [paper]

9. ManyEnt: A Dataset for Few-shot Entity Typing Markus Eberts | Kevin Pech | Adrian Ulges [paper][code]

10. Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification Timo Schick | Helmut Schmid | Hinrich Schütze [paper][code]

11. Flight of the PEGASUS? Comparing Transformers on Few-shot and Zero-shot Multi-document Abstractive Summarization Travis Goodwin | Max Savery | Dina Demner-Fushman [paper][code]

12. Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training Yingyao Wang | Junwei Bao | Guangyi Liu | Youzheng Wu | Xiaodong He | Bowen Zhou | Tiejun Zhao [paper]

13. Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction Haiyang Yu | Ningyu Zhang | Shumin Deng | Hongbin Ye | Wei Zhang | Huajun Chen [paper]

COLING-2021