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title: "Findings of the Association for Computational Linguistics 2022 —"
description: "48 works of Findings of the Association for Computational Linguistics 2022 in our library, the most cited first."
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# Findings of the Association for Computational Linguistics 2022: all papers in our library

48 works · the most cited first · [← Findings of the Association for Computational Linguistics 2022](https://md.research.elamur.ai/free/en/venues/findings/2022/)

1. **[ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://md.research.elamur.ai/free/en/papers/masry-2022-chartqa-benchmark-question-answering-about/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ahmed Masry, Do Xuan Long, Jia Qing Tan, et al.*2022 · 745 citations*
2. **[BBQ: A hand-built bias benchmark for question answering](https://md.research.elamur.ai/free/en/papers/parrish-2022-bbq-hand-built-bias-benchmark/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Alicia Parrish, Angelica Chen, Nikita Nangia, et al.*2022 · 488 citations*
3. **[LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://md.research.elamur.ai/free/en/papers/guo-2022-longt5-efficient-text-text-transformer/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mandy Guo, Joshua Ainslie, David Uthus, et al.*2022 · 398 citations*
4. **[BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla](https://md.research.elamur.ai/free/en/papers/bhattacharjee-2022-banglabert-language-model-pretraining-benchmarks/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Abhik Bhattacharjee, Tahmid Hasan, Wasi Ahmad, et al.*2022 · 296 citations*
5. **[Can language models learn from explanations in context?](https://md.research.elamur.ai/free/en/papers/lampinen-2022-can-language-models-learn-explanations/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Andrew Lampinen, Ishita Dasgupta, Stephanie Chan, et al.*2022 · 290 citations*
6. **[Reframing Instructional Prompts to GPTk's Language](https://md.research.elamur.ai/free/en/papers/mishra-2022-reframing-instructional-prompts-gptk-s/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Swaroop Mishra, Daniel Khashabi, Chitta Baral, et al.*2022 · 236 citations*
7. **[Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models](https://md.research.elamur.ai/free/en/papers/iv-2022-cutting-down-prompts-parameters-simple/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Robert Logan IV, Ivana Balazevic, Eric Wallace, et al.*2022 · 215 citations*
8. **[A Survey on Stance Detection for Mis- and Disinformation Identification](https://md.research.elamur.ai/free/en/papers/hardalov-2022-survey-stance-detection-mis-disinformation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Momchil Hardalov, Arnav Arora, Preslav Nakov, et al.*2022 · 187 citations*
9. **[Prompt-learning for Fine-grained Entity Typing](https://md.research.elamur.ai/free/en/papers/ding-2022-prompt-learning-fine-grained-entity/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ning Ding, Yulin Chen, Xu Han, et al.*2022 · 180 citations*
10. **[Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again](https://md.research.elamur.ai/free/en/papers/gutierrez-2022-thinking-about-gpt-3-context/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Bernal Jimenez Gutierrez, Nikolas McNeal, Clayton Washington, et al.*2022 · 179 citations*
11. **[Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation](https://md.research.elamur.ai/free/en/papers/tan-2022-document-level-relation-extraction-adaptive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qingyu Tan, Ruidan He, Lidong Bing, et al.*2022 · 169 citations*
12. **[Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning](https://md.research.elamur.ai/free/en/papers/razeghi-2022-impact-pretraining-term-frequencies-few/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yasaman Razeghi, Robert L Logan IV, Matt Gardner, et al.*2022 · 162 citations*
13. **[Language Models as Agent Models](https://md.research.elamur.ai/free/en/papers/andreas-2022-language-models-as-agent-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jacob Andreas*2022 · 157 citations*
14. **[UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering](https://md.research.elamur.ai/free/en/papers/oguz-2022-unik-qa-unified-representations-structured/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Barlas Oguz, Xilun Chen, Vladimir Karpukhin, et al.*2022 · 146 citations*
15. **[In-Context Learning for Few-Shot Dialogue State Tracking](https://md.research.elamur.ai/free/en/papers/hu-2022-context-learning-few-shot-dialogue/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yushi Hu, Chia-Hsuan Lee, Tianbao Xie, et al.*2022 · 144 citations*
16. **[RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction](https://md.research.elamur.ai/free/en/papers/chia-2022-relationprompt-leveraging-prompts-generate-synthetic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yew Ken Chia, Lidong Bing, Soujanya Poria, et al.*2022 · 141 citations*
17. **[IndicBART: A Pre-trained Model for Indic Natural Language Generation](https://md.research.elamur.ai/free/en/papers/dabre-2022-indicbart-pre-trained-model-indic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Raj Dabre, Himani Shrotriya, Anoop Kunchukuttan, et al.*2022 · 141 citations*
18. **[Few-Shot Self-Rationalization with Natural Language Prompts](https://md.research.elamur.ai/free/en/papers/marasovic-2022-few-shot-self-rationalization-natural/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ana Marasovic, Iz Beltagy, Doug Downey, et al.*2022 · 134 citations*
19. **[Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion](https://md.research.elamur.ai/free/en/papers/shuster-2022-language-models-that-seek-knowledge/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kurt Shuster, Mojtaba Komeili, Leonard Adolphs, et al.*2022 · 131 citations*
20. **[Extracting Latent Steering Vectors from Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/subramani-2022-extracting-latent-steering-vectors-pretrained/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nishant Subramani, Nivedita Suresh, Matthew Peters*2022 · 131 citations*
21. **[Controllable Natural Language Generation with Contrastive Prefixes](https://md.research.elamur.ai/free/en/papers/qian-2022-controllable-natural-language-generation-contrastive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jing Qian, Li Dong, Yelong Shen, et al.*2022 · 128 citations*
22. **[Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/heffernan-2022-bitext-mining-distilled-sentence-representations/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kevin Heffernan, Onur Çelebi, Holger Schwenk*2022 · 127 citations*
23. **[Logical Fallacy Detection](https://md.research.elamur.ai/free/en/papers/jin-2022-logical-fallacy-detection/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhijing Jin, Abhinav Lalwani, Tejas Vaidhya, et al.*2022 · 119 citations*
24. **[Calibrating Factual Knowledge in Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/dong-2022-calibrating-factual-knowledge-pretrained-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qingxiu Dong, Damai Dai, Yifan Song, et al.*2022 · 115 citations*
25. **[MoEfication: Transformer Feed-forward Layers are Mixtures of Experts](https://md.research.elamur.ai/free/en/papers/zhang-2022-moefication-transformer-feed-forward-layers/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhengyan Zhang, Yankai Lin, Zhiyuan Liu, et al.*2022 · 115 citations*
26. **[Rethinking Document-level Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/sun-2022-rethinking-document-level-neural-machine/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zewei Sun, Mingxuan Wang, Hao Zhou, et al.*2022 · 107 citations*
27. **[On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark](https://md.research.elamur.ai/free/en/papers/sun-2022-safety-conversational-models-taxonomy-dataset/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hao Sun, Guangxuan Xu, Jiawen Deng, et al.*2022 · 102 citations*
28. **[SciFact-Open: Towards open-domain scientific claim verification](https://md.research.elamur.ai/free/en/papers/wadden-2022-scifact-open-open-domain-scientific/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**David Wadden, Kyle Lo, Bailey Kuehl, et al.*2022 · 93 citations*
29. **[Truncation Sampling as Language Model Desmoothing](https://md.research.elamur.ai/free/en/papers/hewitt-2022-truncation-sampling-as-language-model/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**John Hewitt, Christopher Manning, Percy Liang*2022 · 92 citations*
30. **[Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation](https://md.research.elamur.ai/free/en/papers/arora-2022-why-exposure-bias-matters-imitation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kushal Arora, Layla El Asri, Hareesh Bahuleyan, et al.*2022 · 80 citations*
31. **[POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection](https://md.research.elamur.ai/free/en/papers/liu-2022-politics-pretraining-same-story-article/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yujian Liu, Xinliang Frederick Zhang, David Wegsman, et al.*2022 · 70 citations*
32. **[Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction](https://md.research.elamur.ai/free/en/papers/ma-2022-linguistic-rules-based-corpus-generation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shirong Ma, Yinghui Li, Rongyi Sun, et al.*2022 · 59 citations*
33. **[ID10M: Idiom Identification in 10 Languages](https://md.research.elamur.ai/free/en/papers/tedeschi-2022-id10m-idiom-identification-10-languages/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Simone Tedeschi, Federico Martelli, Roberto Navigli*2022 · 49 citations*
34. **[Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/zhou-2022-prompt-based-connective-prediction-method/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hao Zhou, Man Lan, Yuanbin Wu, et al.*2022 · 37 citations*
35. **[Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning](https://md.research.elamur.ai/free/en/papers/zhou-2022-distributed-nli-learning-predict-human/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiang Zhou, Yixin Nie, Mohit Bansal*2022 · 35 citations*
36. **[A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict](https://md.research.elamur.ai/free/en/papers/liu-2022-dual-channel-framework-sarcasm-recognition/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yiyi Liu, Yequan Wang, Aixin Sun, et al.*2022 · 34 citations*
37. **[What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural text](https://md.research.elamur.ai/free/en/papers/fang-2022-what-does-it-take-bake/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Biaoyan Fang, Timothy Baldwin, Karin Verspoor*2022 · 32 citations*
38. **[TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages](https://md.research.elamur.ai/free/en/papers/srinivasan-2022-tydip-dataset-politeness-classification-nine/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Anirudh Srinivasan, Eunsol Choi*2022 · 29 citations*
39. **[The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error](https://md.research.elamur.ai/free/en/papers/atwell-2022-change-that-matters-discourse-parsing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Katherine Atwell, Anthony Sicilia, Seong Jae Hwang, et al.*2022 · 27 citations*
40. **[Encoding and Fusing Semantic Connection and Linguistic Evidence for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/xiang-2022-encoding-fusing-semantic-connection-linguistic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wei Xiang, Bang Wang, Lu Dai, et al.*2022 · 25 citations*
41. **[Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context Experts](https://md.research.elamur.ai/free/en/papers/le-2022-few-shot-anaphora-resolution-scientific/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nghia T. Le, Fan Bai, Alan Ritter*2022 · 20 citations*
42. **[A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing](https://md.research.elamur.ai/free/en/papers/kobayashi-2022-simple-strong-baseline-end-end/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Naoki Kobayashi, Tsutomu Hirao, Hidetaka Kamigaito, et al.*2022 · 16 citations*
43. [**Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations**Kuan-Hao Huang, Varun Iyer, Anoop Kumar, et al.*2022 · 15 citations*](https://md.research.elamur.ai/free/en/papers/huang-2022-unsupervised-syntactically-controlled-paraphrase-generation/)
44. **[Capture Human Disagreement Distributions by Calibrated Networks for Natural Language Inference](https://md.research.elamur.ai/free/en/papers/wang-2022-capture-human-disagreement-distributions-calibrated/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuxia Wang, Minghan Wang, Yimeng Chen, et al.*2022 · 15 citations*
45. **[Sarcasm Detection is Way Too Easy! An Empirical Comparison of Human and Machine Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/farha-2022-sarcasm-detection-is-way-too/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ibrahim Abu Farha, Steven Wilson, Silviu Oprea, et al.*2022 · 13 citations*
46. [**Restoring Hebrew Diacritics Without a Dictionary**Elazar Gershuni, Yuval Pinter*2022 · 9 citations*](https://md.research.elamur.ai/free/en/papers/gershuni-2022-restoring-hebrew-diacritics-without-dictionary/)
47. [**Penn-Helsinki Parsed Corpus of Early Modern English: First Parsing Results and Analysis**Seth Kulick, Neville Ryant, Beatrice Santorini*2022 · 7 citations*](https://md.research.elamur.ai/free/en/papers/kulick-2022-penn-helsinki-parsed-corpus-early/)
48. **[Simple but Challenging: Natural Language Inference Models Fail on Simple Sentences](https://md.research.elamur.ai/free/en/papers/luo-2022-simple-but-challenging-natural-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Cheng Luo, Wei Liu, Jieyu Lin, et al.*2022 · 7 citations*
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