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title: "Findings of the Association for Computational Linguistics"
description: "Findings of the Association for Computational Linguistics 2022 — accepted papers: 48 papers in the Elamur Research library (Abu Dhabi…"
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# Findings of the Association for Computational Linguistics 2022 — accepted papers

Findings of the Association for Computational Linguistics 2022 is the 2022 edition of the Findings of the Association for Computational Linguistics, held in Abu Dhabi, United Arab Emirates, December 2022 (publisher: Association for Computational Linguistics). The ACL Anthology lists 1,132 papers of it; our library holds 48 with our summaries. Below: the most cited papers, every accepted paper, and ours with their topics, authors and institutions.

[**1,132** papers in the proceedings](#all) · [**48** works in our library](https://md.research.elamur.ai/free/en/venues/findings/2022/works/) · [**6,247** citations of these works](https://md.research.elamur.ai/free/en/venues/findings/2022/works/)**Abu Dhabi, United Arab Emirates** where it was held**December 2022** when

## Most cited papers

By their OpenAlex citations; a paper with our summary links to its page here, the others to the original.

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/)**Ahmed Masry, Do Xuan Long, Jia Qing Tan et al.*373 citations · our summary*
2. **[Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models](https://md.research.elamur.ai/free/en/papers/ni-2021-sentence-t5-scalable-sentence-encoders/)**Jianmo Ni, Gustavo Hernandez Abrego, Noah Constant et al.*353 citations · our summary*
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/)**Mandy Guo, Joshua Ainslie, David Uthus et al.*228 citations · our summary*
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/)**Abhik Bhattacharjee, Tahmid Hasan, Wasi Ahmad et al.*182 citations · our summary*
5. **[BBQ: A hand-built bias benchmark for question answering](https://md.research.elamur.ai/free/en/papers/parrish-2022-bbq-hand-built-bias-benchmark/)**Alicia Parrish, Angelica Chen, Nikita Nangia et al.*177 citations · our summary*
6. **[Can language models learn from explanations in context?](https://md.research.elamur.ai/free/en/papers/lampinen-2022-can-language-models-learn-explanations/)**Andrew Lampinen, Ishita Dasgupta, Stephanie Chan et al.*140 citations · our summary*
7. **[Prompt-learning for Fine-grained Entity Typing](https://md.research.elamur.ai/free/en/papers/ding-2022-prompt-learning-fine-grained-entity/)**Ning Ding, Yulin Chen, Xu Han et al.*133 citations · our summary*
8. **[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/)**Robert Logan IV, Ivana Balazevic, Eric Wallace et al.*128 citations · our summary*
9. **[Reframing Instructional Prompts to GPTk's Language](https://md.research.elamur.ai/free/en/papers/mishra-2022-reframing-instructional-prompts-gptk-s/)**Swaroop Mishra, Daniel Khashabi, Chitta Baral et al.*126 citations · our summary*
10. **[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/)**Momchil Hardalov, Arnav Arora, Preslav Nakov et al.*125 citations · our summary*

## All accepted papers (1,132)

The most cited first; 48 with our summary link to their page here, the others to the original.

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/)**Ahmed Masry, Do Xuan Long, Jia Qing Tan et al.*373 citations · our summary*
2. **[Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models](https://md.research.elamur.ai/free/en/papers/ni-2021-sentence-t5-scalable-sentence-encoders/)**Jianmo Ni, Gustavo Hernandez Abrego, Noah Constant et al.*353 citations · our summary*
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/)**Mandy Guo, Joshua Ainslie, David Uthus et al.*228 citations · our summary*
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/)**Abhik Bhattacharjee, Tahmid Hasan, Wasi Ahmad et al.*182 citations · our summary*
5. **[BBQ: A hand-built bias benchmark for question answering](https://md.research.elamur.ai/free/en/papers/parrish-2022-bbq-hand-built-bias-benchmark/)**Alicia Parrish, Angelica Chen, Nikita Nangia et al.*177 citations · our summary*
6. **[Can language models learn from explanations in context?](https://md.research.elamur.ai/free/en/papers/lampinen-2022-can-language-models-learn-explanations/)**Andrew Lampinen, Ishita Dasgupta, Stephanie Chan et al.*140 citations · our summary*
7. **[Prompt-learning for Fine-grained Entity Typing](https://md.research.elamur.ai/free/en/papers/ding-2022-prompt-learning-fine-grained-entity/)**Ning Ding, Yulin Chen, Xu Han et al.*133 citations · our summary*
8. **[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/)**Robert Logan IV, Ivana Balazevic, Eric Wallace et al.*128 citations · our summary*
9. **[Reframing Instructional Prompts to GPTk's Language](https://md.research.elamur.ai/free/en/papers/mishra-2022-reframing-instructional-prompts-gptk-s/)**Swaroop Mishra, Daniel Khashabi, Chitta Baral et al.*126 citations · our summary*
10. **[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/)**Momchil Hardalov, Arnav Arora, Preslav Nakov et al.*125 citations · our summary*
11. **[Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction](https://aclanthology.org/2022.findings-naacl.121/)**Xiang Chen, Ningyu Zhang, Lei Li et al.*125 citations*
12. **[CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection](https://aclanthology.org/2022.findings-naacl.175/)**Zhen Li, Bing Xu, Conghui Zhu et al.*120 citations*
13. **[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/)**Qingyu Tan, Ruidan He, Lidong Bing et al.*120 citations · our summary*
14. **[Reinforced Cross-modal Alignment for Radiology Report Generation](https://aclanthology.org/2022.findings-acl.38/)**Han Qin, Yan Song*120 citations*
15. **[WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation](https://md.research.elamur.ai/free/en/papers/liu-2022-wanli-worker-ai-collaboration-natural/)**Alisa Liu, Swabha Swayamdipta, Noah A. Smith et al.*120 citations · our summary*
16. **[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/)**Bernal Jimenez Gutierrez, Nikolas McNeal, Clayton Washington et al.*115 citations · our summary*
17. **[FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks](https://aclanthology.org/2022.findings-naacl.13/)**Bill Yuchen Lin, Chaoyang He, Zihang Ze et al.*112 citations*
18. **[EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition](https://aclanthology.org/2022.findings-acl.126/)**Zaijing Li, Fengxiao Tang, Ming Zhao et al.*108 citations*
19. **[Are Large Pre-Trained Language Models Leaking Your Personal Information?](https://aclanthology.org/2022.findings-emnlp.148/)**Jie Huang, Hanyin Shao, Kevin Chen-Chuan Chang*102 citations*
20. **[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/)**Yew Ken Chia, Lidong Bing, Soujanya Poria et al.*91 citations · our summary*
21. **[Decomposed Meta-Learning for Few-Shot Named Entity Recognition](https://aclanthology.org/2022.findings-acl.124/)**Tingting Ma, Huiqiang Jiang, Qianhui Wu et al.*89 citations*
22. **[Language Models as Agent Models](https://md.research.elamur.ai/free/en/papers/andreas-2022-language-models-as-agent-models/)**Jacob Andreas*89 citations · our summary*
23. **[BOOKSUM: A Collection of Datasets for Long-form Narrative Summarization](https://aclanthology.org/2022.findings-emnlp.488/)**Wojciech Kryscinski, Nazneen Rajani, Divyansh Agarwal et al.*88 citations*
24. **[Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach](https://aclanthology.org/2022.findings-acl.282/)**Xin Lv, Yankai Lin, Yixin Cao et al.*88 citations*
25. **[BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis](https://aclanthology.org/2022.findings-acl.144/)**Shuo Liang, Wei Wei, Xian-Ling Mao et al.*87 citations*

[See all 1,132 papers in Findings of the Association for Computational Linguistics 2022 — accepted papers →](https://md.research.elamur.ai/free/en/venues/findings/2022/papers/)

## Papers with our summary in our library (48)

The most cited first (OpenAlex citations plus our library's).

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*

[See all 48 works in Findings of the Association for Computational Linguistics 2022 — accepted papers →](https://md.research.elamur.ai/free/en/venues/findings/2022/works/)

## Top topics

Topics of its works in our library, with the number of works on each.

[Language models & pretraining (10)](https://md.research.elamur.ai/free/en/topics/language-models/) · [Reasoning (5)](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Discourse analysis (4)](https://md.research.elamur.ai/free/en/topics/discourse-analysis/) · [Sentiment & emotion (4)](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · [Text generation (4)](https://md.research.elamur.ai/free/en/topics/text-generation/) · [Evaluation & benchmarks (4)](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Question answering (3)](https://md.research.elamur.ai/free/en/topics/question-answering/) · [Dialogue systems (3)](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) · [Coreference resolution (2)](https://md.research.elamur.ai/free/en/topics/coreference-resolution/) · [Machine translation (2)](https://md.research.elamur.ai/free/en/topics/machine-translation/) · [Low-resource languages (2)](https://md.research.elamur.ai/free/en/topics/low-resource-languages/) · [Semitic languages (2)](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · [Bias, fairness & safety (2)](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) · [English (2)](https://md.research.elamur.ai/free/en/topics/english/) · [Named entity recognition (1)](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/)

**10 more topics**

[Treebanks (1)](https://md.research.elamur.ai/free/en/topics/treebanks/) · [Information extraction (1)](https://md.research.elamur.ai/free/en/topics/information-extraction/) · [Hebrew & Aramaic (1)](https://md.research.elamur.ai/free/en/topics/hebrew-aramaic/) · [Information retrieval & search (1)](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · [Semantic parsing & meaning representation (1)](https://md.research.elamur.ai/free/en/topics/semantic-parsing/) · [Efficient models & compression (1)](https://md.research.elamur.ai/free/en/topics/efficient-models/) · [Grammatical error correction & learner language (1)](https://md.research.elamur.ai/free/en/topics/grammatical-error-correction/) · [Idioms & multiword expressions (1)](https://md.research.elamur.ai/free/en/topics/multiword-expressions/) · [Biblical Hebrew (1)](https://md.research.elamur.ai/free/en/topics/biblical-hebrew/) · [Modern Hebrew (1)](https://md.research.elamur.ai/free/en/topics/modern-hebrew/)

## Fields

[Computer Science (48)](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Arts and Humanities (46)](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Social Sciences (5)](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · [Biochemistry, Genetics and Molecular Biology (1)](https://md.research.elamur.ai/free/en/shelves/biochemistry-genetics-and-molecular-biology/) · [Psychology (1)](https://md.research.elamur.ai/free/en/shelves/psychology/)

## Top authors

Authors with a page who published here most often: their works here, and the citations of those works.

[🧑‍🔬**Hao Zhou**3 works here · 246 citations](https://md.research.elamur.ai/free/en/authors/hao-zhou/) · [🧑‍🔬**Sameer Singh**2 works here · 377 citations](https://md.research.elamur.ai/free/en/authors/sameer-singh/) · [🧑‍🔬**Hannaneh Hajishirzi**2 works here · 329 citations](https://md.research.elamur.ai/free/en/authors/hannaneh-hajishirzi/) · [🧑‍🔬**Lidong Bing**2 works here · 310 citations](https://md.research.elamur.ai/free/en/authors/lidong-bing/) · [🧑‍🔬**Zhiyuan Liu**2 works here · 295 citations](https://md.research.elamur.ai/free/en/authors/zhiyuan-liu/) · [🧑‍🔬**Matthew Peters**2 works here · 265 citations](https://md.research.elamur.ai/free/en/authors/matthew-peters/) · [🧑‍🔬**Haitao Zheng**2 works here · 239 citations](https://md.research.elamur.ai/free/en/authors/haitao-zheng/) · [🧑‍🔬**Iz Beltagy**2 works here · 227 citations](https://md.research.elamur.ai/free/en/authors/iz-beltagy/) · [🧑‍🔬**Lei Li**2 works here · 222 citations](https://md.research.elamur.ai/free/en/authors/lei-li/) · [🧑‍🔬**Ahmed Masry**1 work here · 745 citations](https://md.research.elamur.ai/free/en/authors/ahmed-masry/) · [🧑‍🔬**Enamul Hoque**1 work here · 745 citations](https://md.research.elamur.ai/free/en/authors/enamul-hoque/) · [🧑‍🔬**Shafiq Joty**1 work here · 745 citations](https://md.research.elamur.ai/free/en/authors/shafiq-joty/)

## Top institutions

Institutions on the bylines of its works here, by the number of works.

[🔬**Allen Institute for Artificial Intelligence** Seattle · United States 6 works here](https://md.research.elamur.ai/free/en/labs/allen-institute-for-artificial-intelligence/) · [🎓**Tsinghua University** Beijing · China 4 works here](https://md.research.elamur.ai/free/en/universities/tsinghua-university/) · [🏢**Microsoft** Redmond · United States 3 works here](https://md.research.elamur.ai/free/en/companies/microsoft/) · [🎓**University of California, Santa Barbara** Santa Barbara · United States 3 works here](https://md.research.elamur.ai/free/en/universities/university-of-california-santa-barbara/) · [🎓**University of Washington** Seattle · United States 3 works here](https://md.research.elamur.ai/free/en/universities/university-of-washington/) · [🏢**Tencent** Shenzhen · China 3 works here](https://md.research.elamur.ai/free/en/companies/tencent/) · [🎓**University of Edinburgh** Edinburgh · United Kingdom 3 works here](https://md.research.elamur.ai/free/en/universities/university-of-edinburgh/) · [🎓**University of California, Los Angeles** Los Angeles · United States 2 works here](https://md.research.elamur.ai/free/en/universities/university-of-california-los-angeles/) · [🎓**The University of Melbourne** Melbourne · Australia 2 works here](https://md.research.elamur.ai/free/en/universities/the-university-of-melbourne/) · [🎓**Nanyang Technological University** Singapore · Singapore 2 works here](https://md.research.elamur.ai/free/en/universities/nanyang-technological-university/) · [🎓**University of Michigan** Ann Arbor · United States 2 works here](https://md.research.elamur.ai/free/en/universities/university-of-michigan/) · [🎓**ETH Zurich** Zurich · Switzerland 2 works here](https://md.research.elamur.ai/free/en/universities/eth-zurich/)

Place and dates from the event's site or the ACL Anthology; works and citations from OpenAlex and our library.

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