---
title: "Who cites Sanja Fidler — page 2 — 2,087 papers of our library"
description: "2,087 papers of the Elamur Research library cite the works of Sanja Fidler, found in their bibliographies · page 2 of 4 · 101–200; the most cited first."
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---
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# Who cites Sanja Fidler

2,227 citations of their works from 2,087 papers of our library (of the 149,439 whose bibliographies we read) · 344 with a page here, the most cited first · page 2 of 4 · 101–200 · [← Sanja Fidler](https://md.research.elamur.ai/free/en/authors/sanja-fidler/)

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101. **[Semi-Supervised Sequence Modeling with Cross-View Training](https://md.research.elamur.ai/free/en/papers/clark-2018-semi-supervised-sequence-modeling-cross/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kevin Clark, Minh-Thang Luong, Christopher D. Manning, et al.*2018 · 494 citations*
102. [**Rethinking Attention with Performers arXiv:2009.14794**Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, et al.*2020 · 491 citations*](https://md.research.elamur.ai/free/en/papers/choromanski-2020-rethinking-attention-performers/)
103. **[ToTTo: A Controlled Table-To-Text Generation Dataset](https://md.research.elamur.ai/free/en/papers/parikh-2020-totto-controlled-table-text-generation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, et al.*2020 · 482 citations*
104. **[HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training](https://md.research.elamur.ai/free/en/papers/li-2020-hero-hierarchical-encoder-video-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Linjie Li, Yen-Chun Chen, Yu Cheng, et al.*2020 · 481 citations*
105. [**Learning to Generate Reviews and Discovering Sentiment arXiv:1704.01444**Alec Radford, Rafal Jozefowicz, Ilya Sutskever*2017 · 474 citations*](https://md.research.elamur.ai/free/en/papers/radford-2017-learning-generate-reviews-discovering-sentiment/)
106. **[Joint Embedding of Words and Labels for Text Classification](https://md.research.elamur.ai/free/en/papers/wang-2018-joint-embedding-words-labels-text/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guoyin Wang, Chunyuan Li, Wenlin Wang, et al.*2018 · 465 citations*
107. [**CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning arXiv:1612.06890**Justin Johnson, Bharath Hariharan, Laurens van der Maaten, et al.*2016 · 464 citations*](https://md.research.elamur.ai/free/en/papers/johnson-2016-clevr-diagnostic-dataset-compositional-language/)
108. **[LinkBERT: Pretraining Language Models with Document Links](https://md.research.elamur.ai/free/en/papers/yasunaga-2022-linkbert-pretraining-language-models-document/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Michihiro Yasunaga, Jure Leskovec, Percy Liang*2022 · 458 citations*
109. **[ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training](https://md.research.elamur.ai/free/en/papers/qi-2020-prophetnet-predicting-future-n-gram/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Weizhen Qi, Yu Yan, Yeyun Gong, et al.*2020 · 453 citations*
110. **[LLM-Pruner: On the Structural Pruning of Large Language Models](https://md.research.elamur.ai/free/en/papers/ma-2023-llm-pruner-structural-pruning-large/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xinyin Ma, Gongfan Fang, Xinchao Wang*2023 · 452 citations*
111. **[GeDi: Generative Discriminator Guided Sequence Generation](https://md.research.elamur.ai/free/en/papers/krause-2021-gedi-generative-discriminator-guided-sequence/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, et al.*2021 · 449 citations*
112. [**The Falcon Series of Open Language Models arXiv:2311.16867**Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, et al.*2023 · 443 citations*](https://md.research.elamur.ai/free/en/papers/almazrouei-2023-falcon-series-open-language-models/)
113. [**InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks arXiv:2312.14238**Zhe Chen, Jiannan Wu, Wenhai Wang, et al.*2023 · 435 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-internvl-scaling-up-vision/)
114. [**Offline bilingual word vectors, orthogonal transformations and the inverted softmax arXiv:1702.03859**Samuel L. Smith, David H. P. Turban, Steven Hamblin, et al.*2017 · 435 citations*](https://md.research.elamur.ai/free/en/papers/smith-2017-offline-bilingual-word-vectors-orthogonal/)
115. **[Natural Language Inference by Tree-Based Convolution and Heuristic Matching](https://md.research.elamur.ai/free/en/papers/mou-2016-natural-language-inference-tree-based/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lili Mou, Rui Men, Ge Li, et al.*2016 · 430 citations*
116. **[Weight Poisoning Attacks on Pretrained Models](https://md.research.elamur.ai/free/en/papers/kurita-2020-weight-poisoning-attacks-pretrained-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Keita Kurita, Paul Michel, Graham Neubig*2020 · 416 citations*
117. **[Exploiting Source-side Monolingual Data in Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/zhang-2016-exploiting-source-side-monolingual-data/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiajun Zhang, Chengqing Zong*2016 · 409 citations*
118. [**How to Fine-Tune BERT for Text Classification? arXiv:1905.05583**Chi Sun, Xipeng Qiu, Yige Xu, et al.*2019 · 407 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-fine-tune-bert-text-classification/)
119. **[Depression and Self-Harm Risk Assessment in Online Forums](https://md.research.elamur.ai/free/en/papers/yates-2017-depression-self-harm-risk-assessment/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Andrew Yates, Arman Cohan, Nazli Goharian*2017 · 404 citations*
120. **[ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering](https://md.research.elamur.ai/free/en/papers/yu-2019-activitynet-qa-dataset-understanding-complex/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Zhou Yu, Dejing Xu, Jun Yu, et al.*2019 · 403 citations*
121. [**UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training arXiv:2002.12804**Hangbo Bao, Li Dong, Furu Wei, et al.*2020 · 395 citations*](https://md.research.elamur.ai/free/en/papers/bao-2020-unilmv2-pseudo-masked-language-models/)
122. [**Evaluation of Text Generation: A Survey arXiv:2006.14799**Asli Celikyilmaz, Elizabeth Clark, Jianfeng Gao*2020 · 388 citations*](https://md.research.elamur.ai/free/en/papers/celikyilmaz-2020-evaluation-text-generation-survey/)
123. **[What makes a good conversation? How controllable attributes affect human judgments](https://md.research.elamur.ai/free/en/papers/see-2019-what-makes-good-conversation-how/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Abigail See, Stephen Roller, Douwe Kiela, et al.*2019 · 374 citations*
124. **[Parameter-Efficient Transfer Learning with Diff Pruning](https://md.research.elamur.ai/free/en/papers/guo-2021-parameter-efficient-transfer-learning-diff/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Demi Guo, Alexander Rush, Yoon Kim*2021 · 367 citations*
125. **[TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue](https://md.research.elamur.ai/free/en/papers/wu-2020-tod-bert-pre-trained-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chien-Sheng Wu, Steven C.H. Hoi, Richard Socher, et al.*2020 · 361 citations*
126. **[Adversarial Removal of Demographic Attributes from Text Data](https://md.research.elamur.ai/free/en/papers/elazar-2018-adversarial-removal-demographic-attributes-text/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yanai Elazar, Yoav Goldberg*2018 · 359 citations*
127. **[Visual Storytelling](https://md.research.elamur.ai/free/en/papers/huang-2016-visual-storytelling/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ting-Hao Kenneth Huang, Francis Ferraro, Nasrin Mostafazadeh, et al.*2016 · 356 citations*
128. **[Do NLP Models Know Numbers? Probing Numeracy in Embeddings](https://md.research.elamur.ai/free/en/papers/wallace-2019-do-nlp-models-know-numbers/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Eric Wallace, Yizhong Wang, Sujian Li, et al.*2019 · 352 citations*
129. [**Compressive Transformers for Long-Range Sequence Modelling arXiv:1911.05507**Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, et al.*2019 · 340 citations*](https://md.research.elamur.ai/free/en/papers/rae-2019-compressive-transformers-long-range-sequence/)
130. **[Neural Paraphrase Generation with Stacked Residual LSTM Networks](https://md.research.elamur.ai/free/en/papers/prakash-2016-neural-paraphrase-generation-stacked-residual/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Aaditya Prakash, Sadid A. Hasan, Kathy Lee, et al.*2016 · 337 citations*
131. [**Poincaré Embeddings for Learning Hierarchical Representations arXiv:1705.08039**Maximilian Nickel, Douwe Kiela*2017 · 336 citations*](https://md.research.elamur.ai/free/en/papers/nickel-2017-poincare-embeddings-learning-hierarchical-representations/)
132. **[DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension](https://md.research.elamur.ai/free/en/papers/sun-2019-dream-challenge-data-set-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Kai Sun, Dian Yu, Jianshu Chen, et al.*2019 · 334 citations*
133. [**ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation arXiv:2107.02137**Yu Sun, Shuohuan Wang, Shikun Feng, et al.*2021 · 332 citations*](https://md.research.elamur.ai/free/en/papers/sun-2021-ernie-3-0-large/)
134. [**Text and Code Embeddings by Contrastive Pre-Training arXiv:2201.10005**Arvind Neelakantan, Tao Xu, Raul Puri, et al.*2022 · 323 citations*](https://md.research.elamur.ai/free/en/papers/neelakantan-2022-text-code-embeddings-contrastive-pre/)
135. [**Multi-task Sequence to Sequence Learning arXiv:1511.06114**Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, et al.*2015 · 321 citations*](https://md.research.elamur.ai/free/en/papers/luong-2015-multi-task-sequence-sequence-learning/)
136. **[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning](https://md.research.elamur.ai/free/en/papers/srinivasan-2021-wit-wikipedia-based-image-text/) [International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)](https://md.research.elamur.ai/free/en/venues/international-acm-sigir-conference-on-research-and-development-in-information/)**Krishna Srinivasan, Karthik Raman, Jiecao Chen, et al.*2021 · 315 citations*
137. **[Neural Natural Language Inference Models Enhanced with External Knowledge](https://md.research.elamur.ai/free/en/papers/chen-2018-neural-natural-language-inference-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, et al.*2018 · 315 citations*
138. **[A Transformer-based approach to Irony and Sarcasm detection](https://md.research.elamur.ai/free/en/papers/potamias-2019-transformer-based-approach-irony-sarcasm/) [Neural Computing and Applications](https://md.research.elamur.ai/free/en/venues/neural-computing-and-applications/)**Rolandos Alexandros Potamias, Georgios Siolas, Andreas - Georgios Stafylopatis*2019 · 312 citations*
139. **[From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models](https://md.research.elamur.ai/free/en/papers/feng-2023-pretraining-data-language-models-downstream/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shangbin Feng, Chan Young Park, Yuhan Liu, et al.*2023 · 311 citations*
140. **[UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning](https://md.research.elamur.ai/free/en/papers/li-2021-unimo-unified-modal-understanding-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wei Li, Can Gao, Guocheng Niu, et al.*2021 · 308 citations*
141. **[Learning to Write with Cooperative Discriminators](https://md.research.elamur.ai/free/en/papers/holtzman-2018-learning-write-cooperative-discriminators/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ari Holtzman, Jan Buys, Maxwell Forbes, et al.*2018 · 303 citations*
142. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
143. **[Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little](https://md.research.elamur.ai/free/en/papers/sinha-2021-masked-language-modeling-distributional-hypothesis/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Koustuv Sinha, Robin Jia, Dieuwke Hupkes, et al.*2021 · 297 citations*
144. **[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*
145. [**Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling arXiv:2412.05271**Zhe Chen, Weiyun Wang, Yue Cao, et al.*2024 · 287 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-expanding-performance-boundaries-open/)
146. [**ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning arXiv:2002.04326**Weihao Yu, Zihang Jiang, Yanfei Dong, et al.*2020 · 286 citations*](https://md.research.elamur.ai/free/en/papers/yu-2020-reclor-reading-comprehension-dataset-requiring/)
147. **[USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation](https://md.research.elamur.ai/free/en/papers/mehri-2020-usr-unsupervised-reference-free-evaluation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shikib Mehri, Maxine Eskenazi*2020 · 286 citations*
148. [**Cloze-driven Pretraining of Self-attention Networks arXiv:1903.07785**Alexei Baevski, Sergey Edunov, Yinhan Liu, et al.*2019 · 286 citations*](https://md.research.elamur.ai/free/en/papers/baevski-2019-cloze-driven-pretraining-self-attention/)
149. **[Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks](https://md.research.elamur.ai/free/en/papers/thakur-2021-augmented-sbert-data-augmentation-method/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Nandan Thakur, Nils Reimers, Johannes Daxenberger, et al.*2021 · 276 citations*
150. **[DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings](https://md.research.elamur.ai/free/en/papers/chuang-2022-diffcse-difference-based-contrastive-learning/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yung-Sung Chuang, Rumen Dangovski, Hongyin Luo, et al.*2022 · 269 citations*
151. **[Condenser: a Pre-training Architecture for Dense Retrieval](https://md.research.elamur.ai/free/en/papers/gao-2021-condenser-pre-training-architecture-dense/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Luyu Gao, Jamie Callan*2021 · 261 citations*
152. [**Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning**Pradeep Dasigi, Nelson F. Liu, Ana Marasovic, et al.*2019 · 259 citations*](https://md.research.elamur.ai/free/en/papers/dasigi-2019-quoref-reading-comprehension-dataset-questions/)
153. [**SocialIQA: Commonsense Reasoning about Social Interactions arXiv:1904.09728**Maarten Sap, Hannah Rashkin, Derek Chen, et al.*2019 · 259 citations*](https://md.research.elamur.ai/free/en/papers/sap-2019-socialiqa-commonsense-reasoning-about-social/)
154. [**BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model arXiv:1902.04094**Alex Wang, Kyunghyun Cho*2019 · 256 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-bert-has-mouth-must-speak/)
155. [**Contrastive Decoding: Open-ended Text Generation as Optimization arXiv:2210.15097**Xiang Lisa Li, Ari Holtzman, Daniel Fried, et al.*2022 · 254 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-contrastive-decoding-open-ended-text/)
156. **[Generating Datasets with Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/schick-2021-generating-datasets-pretrained-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Timo Schick, Hinrich Schutze*2021 · 254 citations*
157. **[Sentence Centrality Revisited for Unsupervised Summarization](https://md.research.elamur.ai/free/en/papers/zheng-2019-sentence-centrality-revisited-unsupervised-summarization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao Zheng, Mirella Lapata*2019 · 251 citations*
158. [**CLUE: A Chinese Language Understanding Evaluation Benchmark arXiv:2004.05986**Liang Xu, Hai Hu, Xuanwei Zhang, et al.*2020 · 244 citations*](https://md.research.elamur.ai/free/en/papers/xu-2020-clue-chinese-language-understanding/)
159. [**The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision arXiv:1904.12584**Jiayuan Mao, Chuang Gan, Pushmeet Kohli, et al.*2019 · 241 citations*](https://md.research.elamur.ai/free/en/papers/mao-2019-neuro-symbolic-concept-learner-interpreting/)
160. **[Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling](https://md.research.elamur.ai/free/en/papers/han-2019-unsupervised-domain-adaptation-contextualized-embeddings/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xiaochuang Han, Jacob Eisenstein*2019 · 241 citations*
161. [**Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey arXiv:2403.14608**Zeyu Han, Chao Gao, Jinyang Liu, et al.*2024 · 240 citations*](https://md.research.elamur.ai/free/en/papers/han-2024-parameter-efficient-fine-tuning-large/)
162. **[Evaluating Commonsense in Pre-trained Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2019-evaluating-commonsense-pre-trained-language/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Xuhui Zhou, Yue Zhang, Leyang Cui, et al.*2019 · 235 citations*
163. **[AlBERTo: Italian BERT Language Understanding Model for NLP Challenging Tasks Based on Tweets](https://md.research.elamur.ai/free/en/papers/polignano-2019-alberto-italian-bert-language-understanding/) [Italian Conference on Computational Linguistics](https://md.research.elamur.ai/free/en/venues/clicit/)**Marco Polignano, Pierpaolo Basile, Marco de Gemmis, et al.*2019 · 232 citations*
164. **[Probing for semantic evidence of composition by means of simple classification tasks](https://md.research.elamur.ai/free/en/papers/ettinger-2016-probing-semantic-evidence-composition-means/) [Workshop on Evaluating Vector Space Representations for NLP](https://md.research.elamur.ai/free/en/venues/workshop-on-evaluating-vector-space-representations-for-nlp/)**Allyson Ettinger, Ahmed Elgohary, Philip Resnik*2016 · 224 citations*
165. **[MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection](https://md.research.elamur.ai/free/en/papers/zarrella-2016-mitre-semeval-2016-task-6/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Guido Zarrella, Amy Marsh*2016 · 223 citations*
166. **[Unsupervised Question Decomposition for Question Answering](https://md.research.elamur.ai/free/en/papers/perez-2020-unsupervised-question-decomposition-question-answering/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ethan Perez, Patrick Lewis, Wen-tau Yih, et al.*2020 · 222 citations*
167. **[What Happens To BERT Embeddings During Fine-tuning?](https://md.research.elamur.ai/free/en/papers/merchant-2020-what-happens-bert-embeddings-during/) [BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/blackboxnlp/)**Amil Merchant, Elahe Rahimtoroghi, Ellie Pavlick, et al.*2020 · 220 citations*
168. [**Transforming Question Answering Datasets Into Natural Language Inference Datasets arXiv:1809.02922**Dorottya Demszky, Kelvin Guu, Percy Liang*2018 · 220 citations*](https://md.research.elamur.ai/free/en/papers/demszky-2018-transforming-question-answering-datasets-natural/)
169. [**Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models arXiv:2308.16149**Neha Sengupta, Sunil Kumar Sahu, Bokang Jia, et al.*2023 · 218 citations*](https://md.research.elamur.ai/free/en/papers/sengupta-2023-jais-jais-chat-arabic-centric/)
170. **[The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants](https://md.research.elamur.ai/free/en/papers/habernal-2018-argument-reasoning-comprehension-task-identification/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ivan Habernal, Henning Wachsmuth, Iryna Gurevych, et al.*2018 · 217 citations*
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