--- title: "Papers of 2021 — page 3" description: "Papers of 2021: 504 papers of the Elamur Research library — what each work is about in our own words, its ideas, references and who cites it." canonical: "https://research.elamur.ai/free/en/papers/2021/page/3/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/2021/page/3/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / 2021 # Papers of 2021 — page 3 504 papers · [all years](https://md.research.elamur.ai/free/en/papers/) [← prev](https://md.research.elamur.ai/free/en/papers/2021/page/2/) [1](https://md.research.elamur.ai/free/en/papers/2021/) [2](https://md.research.elamur.ai/free/en/papers/2021/page/2/) **3** [4](https://md.research.elamur.ai/free/en/papers/2021/page/4/) [5](https://md.research.elamur.ai/free/en/papers/2021/page/5/) [6](https://md.research.elamur.ai/free/en/papers/2021/page/6/) [next →](https://md.research.elamur.ai/free/en/papers/2021/page/4/) - [Generating Datasets with Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/schick-2021-generating-datasets-pretrained-language-models/) — Timo Schick, Hinrich Schutze · arXiv:2104.07540 - [Generic Oracles for Structured Prediction](https://md.research.elamur.ai/free/en/papers/ud-2021-teichmann-generic-oracles-structured-prediction/) — Christoph Teichmann, Antoine Venant · English - [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://md.research.elamur.ai/free/en/papers/schuster-2021-get-your-vitamin-c-robust/) — Tal Schuster, Adam Fisch, Regina Barzilay · Proceedings of the 2021 Conference of the North… - [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/) — Nan Du, Yanping Huang, Andrew M. Dai, et al. · arXiv:2112.06905 - [Glancing Transformer for Non-Autoregressive Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/qian-2021-glancing-transformer-non-autoregressive-neural/) — Lihua Qian, Hao Zhou, Yu Bao, et al. · Proceedings of the 59th Annual Meeting of the… - [GPT Understands, Too](https://md.research.elamur.ai/free/en/papers/liu-2021-gpt-understands-too/) — Xiao Liu, Yanan Zheng, Zhengxiao Du, et al. · arXiv:2103.10385 - [GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation](https://md.research.elamur.ai/free/en/papers/yoo-2021-gpt3mix-leveraging-large-scale-language/) — Kang Min Yoo, Dongju Park, Jaewook Kang, et al. · Findings of the Association for Computational… - [Gradient-based Adversarial Attacks against Text Transformers](https://md.research.elamur.ai/free/en/papers/guo-2021-gradient-based-adversarial-attacks-against/) — Chuan Guo, Alexandre Sablayrolles, Hervé Jégou, et al. · Proceedings of the 2021 Conference on Empirical… - [Grammar-Based Concept Alignment for Domain-Specific Machine Translation](https://md.research.elamur.ai/free/en/papers/masciolini-2021-grammar-based-concept-alignment-domain/) — Arianna Masciolini, Aarne Ranta · Machine translation - [Graph Rewriting for Enhanced Universal Dependencies](https://md.research.elamur.ai/free/en/papers/ud-2021-guillaume-graph-rewriting-enhanced-universal-dependencies/) — Bruno Guillaume, Guy Perrier · English - [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://md.research.elamur.ai/free/en/papers/guo-2021-graphcodebert-pre-training-code-representations/) — Daya Guo, Shuo Ren, Shuai Lu, et al. · arXiv:2009.08366 - [GRICE: A Grammar-based Dataset for Recovering Implicature and Conversational rEasoning](https://md.research.elamur.ai/free/en/papers/zheng-2021-grice-grammar-based-dataset-recovering/) — Zilong Zheng, Shuwen Qiu, Lifeng Fan, et al. · English - [GSum: A General Framework for Guided Neural Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/dou-2021-gsum-general-framework-guided-neural/) — Zi-Yi Dou, Pengfei Liu, Hiroaki Hayashi, et al. · Proceedings of the 2021 Conference of the North… - [Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies](https://md.research.elamur.ai/free/en/papers/dev-2021-harms-gender-exclusivity-challenges-non/) — Sunipa Dev, Masoud Monajatipoor, Anaelia Ovalle, et al. · Proceedings of the 2021 Conference on Empirical… - [Hate Towards the Political Opponent: A Twitter Corpus Study of the 2020 US Elections on the Basis of Offensive Speech and Stance Detection](https://md.research.elamur.ai/free/en/papers/grimminger-2021-hate-political-opponent-twitter-corpus/) — Lara Grimminger, Roman Klinger · Proceedings of the Eleventh Workshop on… - [HateBERT: Retraining BERT for Abusive Language Detection in English](https://md.research.elamur.ai/free/en/papers/caselli-2021-hatebert-retraining-bert-abusive-language/) — Tommaso Caselli, Valerio Basile, Jelena Mitrović, et al. · Proceedings of the 5th Workshop on Online Abuse… - [HateCheck: Functional Tests for Hate Speech Detection Models](https://md.research.elamur.ai/free/en/papers/rottger-2021-hatecheck-functional-tests-hate-speech/) — Paul Röttger, Bertie Vidgen, Dong Nguyen, et al. · Proceedings of the 59th Annual Meeting of the… - [He Thinks He Knows Better than the Doctors: BERT for Event Factuality Fails on Pragmatics](https://md.research.elamur.ai/free/en/papers/jiang-2021-he-thinks-he-knows-better/) — Nanjiang Jiang, Marie-Catherine de Marneffe · English - [HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition](https://md.research.elamur.ai/free/en/papers/langstream-2021-chriqui-hebert-hebemo-hebrew-bert-model/) — Avihay Chriqui, Inbal Yahav · Modern Hebrew - [Hebrew Psychological Lexicons](https://md.research.elamur.ai/free/en/papers/aclbulk-2021-shapira-hebrew-psychological-lexicons/) — Natalie Shapira, Dana Atzil-Slonim, Daniel Juravski, et al. · Modern Hebrew - [Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger](https://md.research.elamur.ai/free/en/papers/qi-2021-hidden-killer-invisible-textual-backdoor/) — Fanchao Qi, Mukai Li, Yangyi Chen, et al. · Proceedings of the 59th Annual Meeting of the… - [Highly accurate protein structure prediction with AlphaFold](https://md.research.elamur.ai/free/en/papers/jumper-2021-highly-accurate-protein-structure-prediction/) — John Jumper, Richard Evans, Alexander Pritzel, et al. · Nature - [HONEST: Measuring Hurtful Sentence Completion in Language Models](https://md.research.elamur.ai/free/en/papers/nozza-2021-honest-measuring-hurtful-sentence-completion/) — Debora Nozza, Federico Bianchi, Dirk Hovy · Proceedings of the 2021 Conference of the North… - [How focus particles and accents affect attachment](https://md.research.elamur.ai/free/en/papers/carlson-2021-how-focus-particles-accents-affect/) — Katy Carlson, David Potter · English - [How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models](https://md.research.elamur.ai/free/en/papers/rust-2021-how-good-is-your-tokenizer/) — Phillip Rust, Jonas Pfeiffer, Ivan Vulić, et al. · Proceedings of the 59th Annual Meeting of the… - [How many data points is a prompt worth?](https://md.research.elamur.ai/free/en/papers/scao-2021-how-many-data-points-is/) — Teven Le Scao, Alexander Rush · Proceedings of the 2021 Conference of the North… - [How Matthew quotes the scripture: Analysis of the two old testament quotations in the Gospel of Matthew](https://md.research.elamur.ai/free/en/papers/s-2021-matthew-quotes-scripture-analysis-two/) — Ivić Branislav S. · Reči (Beograd) 12 - [How useful are Enhanced Universal Dependencies for semantic interpretation?](https://md.research.elamur.ai/free/en/papers/ud-2021-findlay-how-useful-are-enhanced-universal/) — Jamie Y. Findlay, Dag T. T. Haug · English - [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://md.research.elamur.ai/free/en/papers/hsu-2021-hubert-self-supervised-speech-representation/) — Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, et al. · arXiv:2106.07447 - [Human Rationales as Attribution Priors for Explainable Stance Detection](https://md.research.elamur.ai/free/en/papers/jayaram-2021-human-rationales-as-attribution-priors/) — Sahil Jayaram, Emily Allaway · Proceedings of the 2021 Conference on Empirical… - [Hurdles to Progress in Long-form Question Answering](https://md.research.elamur.ai/free/en/papers/krishna-2021-hurdles-progress-long-form-question/) — Kalpesh Krishna, Aurko Roy, Mohit Iyyer · arXiv:2103.06332 - [Hybrid Characteristics of Prefixed Verbs in Yiddish](https://md.research.elamur.ai/free/en/papers/shishigin-2021-hybrid-characteristics-prefixed-verbs-yiddish/) — K. A. Shishigin · Вестник Кемеровского государственного… - [I-BERT: Integer-only BERT Quantization](https://md.research.elamur.ai/free/en/papers/kim-2021-i-bert-integer-only-bert/) — Sehoon Kim, Amir Gholami, Zhewei Yao, et al. · arXiv:2101.01321 · ICML 2021 (Oral) - [iCompass at Shared Task on Sarcasm and Sentiment Detection in Arabic](https://md.research.elamur.ai/free/en/papers/naski-2021-icompass-shared-task-sarcasm-sentiment/) — Malek Naski, Abir Messaoudi, Hatem Haddad, et al. · Proceedings of the Sixth Arabic Natural… - [Idiomatic Expression Identification using Semantic Compatibility](https://md.research.elamur.ai/free/en/papers/zeng-2021-idiomatic-expression-identification-semantic-compatibility/) — Ziheng Zeng, Suma Bhat · Research library - [ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation](https://md.research.elamur.ai/free/en/papers/malik-2021-ildc-cjpe-indian-legal-documents/) — Vijit Malik, Rishabh Sanjay, Shubham Kumar Nigam, et al. · Proceedings of the 59th Annual Meeting of the… - [Improving Arabic Diacritization with Regularized Decoding and Adversarial Training](https://md.research.elamur.ai/free/en/papers/acl-2021-qin-improving-arabic-diacritization-regularized-decoding/) — Han Qin, Guimin Chen, Yuanhe Tian, et al. · Semitic languages - [Improving Multi-Party Dialogue Discourse Parsing via Domain Integration](https://md.research.elamur.ai/free/en/papers/liu-2021-improving-multi-party-dialogue-discourse/) — Zhengyuan Liu, Nancy Chen · Proceedings of the 2nd Workshop on… - [Improving Stance Detection with Multi-Dataset Learning and Knowledge Distillation](https://md.research.elamur.ai/free/en/papers/li-2021-improving-stance-detection-multi-dataset/) — Yingjie Li, Chenye Zhao, Cornelia Caragea · Proceedings of the 2021 Conference on Empirical… - [Incorporating Syntax and Semantics in Coreference Resolution with Heterogeneous Graph Attention Network](https://md.research.elamur.ai/free/en/papers/jiang-2021-incorporating-syntax-semantics-coreference-resolution/) — Fan Jiang, Trevor Cohn · Proceedings of the 2021 Conference of the North… - [Integrating Deep Event-Level and Script-Level Information for Script Event Prediction](https://md.research.elamur.ai/free/en/papers/bai-2021-integrating-deep-event-level-script/) — Long Bai, Saiping Guan, Jiafeng Guo, et al. · Proceedings of the 2021 Conference on Empirical… - [Integrating Transformers and Knowledge Graphs for Twitter Stance Detection](https://md.research.elamur.ai/free/en/papers/clark-2021-integrating-transformers-knowledge-graphs-twitter/) — Thomas Clark, Costanza Conforti, Fangyu Liu, et al. · Proceedings of the Seventh Workshop on Noisy… - [Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning](https://md.research.elamur.ai/free/en/papers/lu-2021-inter-gps-interpretable-geometry-problem/) — Pan Lu, Ran Gong, Shibiao Jiang, et al. · arXiv:2105.04165 - [Intrinsic Bias Metrics Do Not Correlate with Application Bias](https://md.research.elamur.ai/free/en/papers/tarrant-2021-intrinsic-bias-metrics-do-not/) — Seraphina Goldfarb-Tarrant, Rebecca Marchant, Ricardo Muñoz Sánchez, et al. · Proceedings of the 59th Annual Meeting of the… - [Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning](https://md.research.elamur.ai/free/en/papers/aghajanyan-2021-intrinsic-dimensionality-explains-effectiveness-language/) — Armen Aghajanyan, Sonal Gupta, Luke Zettlemoyer · Proceedings of the 59th Annual Meeting of the… - [Investigating Pretrained Language Models for Graph-to-Text Generation](https://md.research.elamur.ai/free/en/papers/ribeiro-2021-investigating-pretrained-language-models-graph/) — Leonardo F. R. Ribeiro, Martin Schmitt, Hinrich Schütze, et al. · Proceedings of the 3rd Workshop on Natural… - [Is Stance Detection Topic-Independent and Cross-topic Generalizable? - A Reproduction Study](https://md.research.elamur.ai/free/en/papers/reuver-2021-is-stance-detection-topic-independent/) — Myrthe Reuver, Suzan Verberne, Roser Morante, et al. · Proceedings of the 8th Workshop on Argument… - [Is there less annotator agreement when the discourse relation is underspecified?](https://md.research.elamur.ai/free/en/papers/hoek-2021-is-there-less-annotator-agreement/) — Jet Hoek, Merel C.J. Scholman, Ted J.M. Sanders · Proceedings of the First Workshop on… - [Issayas Tesfamariam, ትግርኛ ንዛረብ: Let’s Speak Tigrinya, Elementary Level, A First-Year Textbook, A Multidimensional Approach to the Teaching and Learning of Tigrinya As a Foreign Language](https://md.research.elamur.ai/free/en/papers/voigt-2021-issayas-tesfamariam-lets-speak-tigrinya/) — Rainer Voigt · Aethiopica 23 - [It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://md.research.elamur.ai/free/en/papers/schick-2021-it-s-not-just-size/) — Timo Schick, Hinrich Schütze · Proceedings of the 2021 Conference of the North… - [JABER and SABER: Junior and Senior Arabic BERt](https://md.research.elamur.ai/free/en/papers/ghaddar-2021-jaber-saber-junior-senior-arabic/) — Abbas Ghaddar, Yimeng Wu, Ahmad Rashid, et al. · arXiv:2112.04329 - [Joint Universal Syntactic and Semantic Parsing](https://md.research.elamur.ai/free/en/papers/eskin-2021-joint-universal-syntactic-semantic-parsing/) — Elias Stengel-Eskin, Kenton Murray, Sheng Zhang, et al. · Research library - [Knowledge Enhanced Masked Language Model for Stance Detection](https://md.research.elamur.ai/free/en/papers/kawintiranon-2021-knowledge-enhanced-masked-language-model/) — Kornraphop Kawintiranon, Lisa Singh · Proceedings of the 2021 Conference of the North… - [Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training](https://md.research.elamur.ai/free/en/papers/agarwal-2021-knowledge-graph-based-synthetic-corpus/) — Oshin Agarwal, Heming Ge, Siamak Shakeri, et al. · Proceedings of the 2021 Conference of the North… - [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://md.research.elamur.ai/free/en/papers/schuhmann-2021-laion-400m-open-dataset-clip/) — Christoph Schuhmann, Richard Vencu, Romain Beaumont, et al. · arXiv:2111.02114 - [Language Models are Few-shot Multilingual Learners](https://md.research.elamur.ai/free/en/papers/winata-2021-language-models-are-few-shot/) — Genta Indra Winata, Andrea Madotto, Zhaojiang Lin, et al. · Proceedings of the 1st Workshop on Multilingual… - [Large Dual Encoders Are Generalizable Retrievers](https://md.research.elamur.ai/free/en/papers/ni-2021-large-dual-encoders-generalizable-retrievers/) — Jianmo Ni, Chen Qu, Jing Lu, et al. · arXiv:2112.07899 - [Large-scale text pre-training helps with dialogue act recognition, but not without fine-tuning](https://md.research.elamur.ai/free/en/papers/noble-2021-large-scale-text-pre-training/) — Bill Noble, Vladislav Maraev · English - [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://md.research.elamur.ai/free/en/papers/goyal-2021-larger-scale-transformers-multilingual-masked/) — Naman Goyal, Jingfei Du, Myle Ott, et al. · Proceedings of the 6th Workshop on… - [Latent Hatred: A Benchmark for Understanding Implicit Hate Speech](https://md.research.elamur.ai/free/en/papers/elsherief-2021-latent-hatred-benchmark-understanding-implicit/) — Mai ElSherief, Caleb Ziems, David Muchlinski, et al. · Proceedings of the 2021 Conference on Empirical… - [LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding](https://md.research.elamur.ai/free/en/papers/xu-2021-layoutlmv2-multi-modal-pre-training/) — Yang Xu, Yiheng Xu, Tengchao Lv, et al. · Proceedings of the 59th Annual Meeting of the… - [Learning Compact Metrics for MT](https://md.research.elamur.ai/free/en/papers/pu-2021-learning-compact-metrics-mt/) — Amy Pu, Hyung Won Chung, Ankur Parikh, et al. · Proceedings of the 2021 Conference on Empirical… - [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://md.research.elamur.ai/free/en/papers/vidgen-2021-learning-worst-dynamically-generated-datasets/) — Bertie Vidgen, Tristan Thrush, Zeerak Waseem, et al. · Proceedings of the 59th Annual Meeting of the… - [Learning How to Ask: Querying LMs with Mixtures of Soft Prompts](https://md.research.elamur.ai/free/en/papers/qin-2021-learning-ask-querying-lms-mixtures/) — Guanghui Qin, Jason Eisner · arXiv:2104.06599 · NAACL-HLT 2021 - [Learning To Retrieve Prompts for In-Context Learning](https://md.research.elamur.ai/free/en/papers/rubin-2021-learning-retrieve-prompts-context-learning/) — Ohad Rubin, Jonathan Herzig, Jonathan Berant · arXiv:2112.08633 - [Learning Transferable Visual Models From Natural Language Supervision](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/) — Alec Radford, Jong Wook Kim, Chris Hallacy, et al. · arXiv:2103.00020 - [Lemmatisation, etymology and information overload on English and Swedish editions of Wiktionary](https://md.research.elamur.ai/free/en/papers/verdizade-2021-wiktionary-en-sv/) — Allahverdi Verdizade · English - [Let's be explicit about that: Distant supervision for implicit discourse relation classification via connective prediction](https://md.research.elamur.ai/free/en/papers/kurfal-2021-let-s-be-explicit-about/) — Murathan Kurfalı, Robert Östling · Proceedings of the 1st Workshop on… - [Leveraging Offensive Language for Sarcasm and Sentiment Detection in Arabic](https://md.research.elamur.ai/free/en/papers/husain-2021-leveraging-offensive-language-sarcasm-sentiment/) — Fatemah Husain, Ozlem Uzuner · Proceedings of the Sixth Arabic Natural… - [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://md.research.elamur.ai/free/en/papers/izacard-2021-leveraging-passage-retrieval-generative-models/) — Gautier Izacard, Edouard Grave · Proceedings of the 16th Conference of the… - [LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://md.research.elamur.ai/free/en/papers/chalkidis-2021-lexglue-benchmark-dataset-legal-language/) — Ilias Chalkidis; Abhik Jana; Dirk Hartung; Michael Bommarito; Ion Androutsopoulos; Daniel Martin Katz;, Nikolaos Aletras · arXiv:2110.00976 - [MacBERTh: Development and Evaluation of a Historically Pre-trained Language Model for English (1450-1950)](https://md.research.elamur.ai/free/en/papers/arevalo-2021-macberth-development-evaluation-historically-pre/) — Enrique Manjavacas Arevalo, Lauren Fonteyn · English - [Machine Learning-Based Model for Sentiment and Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/nayel-2021-machine-learning-based-model-sentiment/) — Hamada Nayel, Eslam Amer, Aya Allam, et al. · Proceedings of the Sixth Arabic Natural… - [Maintaining Common Ground in Dynamic Environments](https://md.research.elamur.ai/free/en/papers/udagawa-2021-maintaining-common-ground-dynamic-environments/) — Takuma Udagawa, Akiko Aizawa · Research library - [Making Pre-trained Language Models Better Few-shot Learners](https://md.research.elamur.ai/free/en/papers/gao-2021-making-pre-trained-language-models/) — Tianyu Gao, Adam Fisch, Danqi Chen · Proceedings of the 59th Annual Meeting of the… - [Manually Annotated Spelling Error Corpus for Amharic](https://md.research.elamur.ai/free/en/papers/gezmu-2021-manually-annotated-spelling-error-corpus/) — Andargachew Mekonnen Gezmu, Tirufat Tesifaye Lema, Binyam Ephrem Seyoum, et al. · arXiv:2106.13521 - [MasakhaNER: Named Entity Recognition for African Languages](https://md.research.elamur.ai/free/en/papers/adelani-2021-masakhaner-named-entity-recognition-african/) — David Ifeoluwa Adelani, Jade Abbott, Graham Neubig, et al. · Transactions of the Association for… - [Masked Autoencoders Are Scalable Vision Learners](https://md.research.elamur.ai/free/en/papers/he-2021-masked-autoencoders-scalable-vision-learners/) — Kaiming He, Xinlei Chen, Saining Xie, et al. · arXiv:2111.06377 - [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/) — Koustuv Sinha, Robin Jia, Dieuwke Hupkes, et al. · Proceedings of the 2021 Conference on Empirical… - [Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP](https://md.research.elamur.ai/free/en/papers/goot-2021-massive-choice-ample-tasks-machamp/) — Rob van der Goot, Ahmet Üstün, Alan Ramponi, et al. · Proceedings of the 16th Conference of the… - [MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers](https://md.research.elamur.ai/free/en/papers/pillutla-2021-mauve-measuring-gap-between-neural/) — Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, et al. · arXiv:2102.01454 - [Measuring Association Between Labels and Free-Text Rationales](https://md.research.elamur.ai/free/en/papers/wiegreffe-2021-measuring-association-between-labels-free/) — Sarah Wiegreffe, Ana Marasović, Noah A. Smith · Proceedings of the 2021 Conference on Empirical… - [Measuring Coding Challenge Competence With APPS](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-measuring-coding-challenge-competence-apps/) — Dan Hendrycks, Steven Basart, Saurav Kadavath, et al. · arXiv:2105.09938 - [Measuring Mathematical Problem Solving With the MATH Dataset](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-measuring-mathematical-problem-solving-math/) — Dan Hendrycks, Collin Burns, Saurav Kadavath, et al. · arXiv:2103.03874 - [MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization](https://md.research.elamur.ai/free/en/papers/zhu-2021-mediasum-large-scale-media-interview/) — Chenguang Zhu, Yang Liu, Jie Mei, et al. · Proceedings of the 2021 Conference of the North… - [Merging Models with Fisher-Weighted Averaging](https://md.research.elamur.ai/free/en/papers/matena-2021-merging-models-fisher-weighted-averaging/) — Michael Matena, Colin Raffel · arXiv:2111.09832 - [Metacommunicative-why fragments as probes into the grammar of the speech act layer](https://md.research.elamur.ai/free/en/papers/woods-2021-metacommunicative-i-why-i-fragments/) — Rebecca Woods, Luis Vicente · English - [MIDAS: A Dialog Act Annotation Scheme for Open Domain Human-Machine Spoken Conversations](https://md.research.elamur.ai/free/en/papers/yu-2021-midas-dialog-act-annotation-scheme/) — Dian Yu, Zhou Yu · English - [Mind the Style of Text! 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