--- title: "Papers of 2022 — page 1" description: "Papers of 2022: 343 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/2022/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/2022/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / 2022 # Papers of 2022 — page 1 343 papers · [all years](https://md.research.elamur.ai/free/en/papers/) **1** [2](https://md.research.elamur.ai/free/en/papers/2022/page/2/) [3](https://md.research.elamur.ai/free/en/papers/2022/page/3/) [4](https://md.research.elamur.ai/free/en/papers/2022/page/4/) - ["You might think about slightly revising the title": Identifying Hedges in Peer-tutoring Interactions](https://md.research.elamur.ai/free/en/papers/raphalen-2022-you-might-think-about-slightly/) — Yann Raphalen, Chloé Clavel, Justine Cassell · English - [A Dependency Treebank of Spoken Second Language English](https://md.research.elamur.ai/free/en/papers/kyle-2022-dependency-treebank-spoken-second-language/) — Kristopher Kyle, Masaki Eguchi, Aaron Miller, et al. · Mova's core list - [A Distance-Aware Multi-Task Framework for Conversational Discourse Parsing](https://md.research.elamur.ai/free/en/papers/fan-2022-distance-aware-multi-task-framework/) — Yaxin Fan, Peifeng Li, Fang Kong, et al. · Proceedings of the 29th International… - [A Double-Graph Based Framework for Frame Semantic Parsing](https://md.research.elamur.ai/free/en/papers/zheng-2022-double-graph-based-framework-frame/) — Ce Zheng, Xudong Chen, Runxin Xu, et al. · Proceedings of the 2022 Conference of the North… - [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/) — Yiyi Liu, Yequan Wang, Aixin Sun, et al. · Findings of the Association for Computational… - [A history of English](https://md.research.elamur.ai/free/en/papers/hejna-2022-history-english/) — Míša Hejná, George Walkden · English - [A Holistic Approach to Undesired Content Detection in the Real World](https://md.research.elamur.ai/free/en/papers/markov-2022-holistic-approach-undesired-content-detection/) — Todor Markov, Chong Zhang, Sandhini Agarwal, et al. · arXiv:2208.03274 - [A Learning-Based Dependency to Constituency Conversion Algorithm for the Turkish Language](https://md.research.elamur.ai/free/en/papers/an-2022-learning-based-dependency-constituency-conversion/) — Büşra Marşan, Oğuz K. Yıldız, Aslı Kuzgun, et al. · Proceedings of the Thirteenth Language… - [A Multimodal Corpus for Emotion Recognition in Sarcasm](https://md.research.elamur.ai/free/en/papers/ray-2022-multimodal-corpus-emotion-recognition-sarcasm/) — Anupama Ray, Shubham Mishra, Apoorva Nunna, et al. · Proceedings of the Thirteenth Language… - [A Paradigm Change for Formal Syntax: Computational Algorithms in the Grammar of English](https://md.research.elamur.ai/free/en/papers/ninio-2022-paradigm-change-formal-syntax-computational/) — Anat Ninio · English - [A Pragmatics-Centered Evaluation Framework for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/sileo-2022-pragmatics-centered-evaluation-framework-natural/) — Damien Sileo, Philippe Muller, Tim Van de Cruys, et al. · Proceedings of the Thirteenth Language… - [A Second Wave of UD Hebrew Treebanking and Cross-Domain Parsing](https://md.research.elamur.ai/free/en/papers/ud-2022-zeldes-second-wave-ud-hebrew-treebanking/) — Amir Zeldes, Nick Howell, Noam Ordan, et al. · Modern Hebrew - [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/) — Naoki Kobayashi, Tsutomu Hirao, Hidetaka Kamigaito, et al. · Findings of the Association for Computational… - [A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives](https://md.research.elamur.ai/free/en/papers/zeng-2022-survey-automatic-irony-processing-linguistic/) — Qingcheng Zeng, An-Ran Li · English - [A Survey on Automated Fact-Checking](https://md.research.elamur.ai/free/en/papers/guo-2022-survey-automated-fact-checking/) — Zhijiang Guo, Michael Schlichtkrull, Andreas Vlachos · Transactions of the Association for… - [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. · Findings of the Association for Computational… - [A Universal Dependencies Treebank of Ancient Hebrew](https://md.research.elamur.ai/free/en/papers/ud-2022-swanson-universal-dependencies-treebank-ancient-hebrew/) — Daniel G. Swanson, Francis M. Tyers · Ancient Hebrew - [A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge](https://md.research.elamur.ai/free/en/papers/schwenk-2022-okvqa-benchmark-visual-question-answering/) — Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, et al. · arXiv:2206.01718 - [ACES: Translation Accuracy Challenge Sets for Evaluating Machine Translation Metrics](https://md.research.elamur.ai/free/en/papers/amrhein-2022-aces-translation-accuracy-challenge-sets/) — Chantal Amrhein, Nikita Moghe, Liane Guillou · Machine translation - [Active Example Selection for In-Context Learning](https://md.research.elamur.ai/free/en/papers/zhang-2022-active-example-selection-context-learning/) — Yiming Zhang, Shi Feng, Chenhao Tan · Proceedings of the 2022 Conference on Empirical… - [Adapting Coreference Resolution Models through Active Learning](https://md.research.elamur.ai/free/en/papers/yuan-2022-adapting-coreference-resolution-models-through/) — Michelle Yuan, Patrick Xia, Chandler May, et al. · Proceedings of the 60th Annual Meeting of the… - [Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning](https://md.research.elamur.ai/free/en/papers/alabi-2022-adapting-pre-trained-language-models/) — Jesujoba O. Alabi, David Ifeoluwa Adelani, Marius Mosbach, et al. · Proceedings of the 29th International… - [AlephBERT: Language Model Pre-training and Evaluation from Sub-Word to Sentence Level](https://md.research.elamur.ai/free/en/papers/acl-2022-seker-alephbert-language-model-pre-training/) — Amit Seker, Elron Bandel, Dan Bareket, et al. · Modern Hebrew - [Analysis of Participants in the Second Major Division of Numbers](https://md.research.elamur.ai/free/en/papers/hbo-2022-jin-analysis-participants-second-major-division/) — Gyusang Jin · Ancient Hebrew - [Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection](https://md.research.elamur.ai/free/en/papers/sap-2022-annotators-attitudes-how-annotator-beliefs/) — Maarten Sap, Swabha Swayamdipta, Laura Vianna, et al. · Proceedings of the 2022 Conference of the North… - [ASQA: Factoid Questions Meet Long-Form Answers](https://md.research.elamur.ai/free/en/papers/stelmakh-2022-asqa-factoid-questions-meet-long/) — Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, et al. · Proceedings of the 2022 Conference on Empirical… - [Assessing the Linguistic Knowledge in Arabic Pre-trained Language Models Using Minimal Pairs](https://md.research.elamur.ai/free/en/papers/alrajhi-2022-assessing-linguistic-knowledge-arabic-pre/) — Wafa Abdullah Alrajhi, Hend Al-Khalifa, Abdulmalik AlSalman · Proceedings of the Seventh Arabic Natural… - [Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models](https://md.research.elamur.ai/free/en/papers/bohnet-2022-attributed-question-answering-evaluation-modeling/) — Bernd Bohnet, Vinh Q. Tran, Pat Verga, et al. · arXiv:2212.08037 - [AudioLM: a Language Modeling Approach to Audio Generation](https://md.research.elamur.ai/free/en/papers/borsos-2022-audiolm-language-modeling-approach-audio/) — Zalan Borsos, Raphael Marinier, Damien Vincent, et al. · arXiv:2209.03143 - [Autoformalization with Large Language Models](https://md.research.elamur.ai/free/en/papers/wu-2022-autoformalization-large-language-models/) — Yuhuai Wu, Albert Q. Jiang, Wenda Li, et al. · arXiv:2205.12615 - [Automatic Chain of Thought Prompting in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2022-automatic-chain-thought-prompting-large/) — Zhuosheng Zhang, Aston Zhang, Mu Li, et al. · arXiv:2210.03493 - [Automatic Dialect Density Estimation for African American English](https://md.research.elamur.ai/free/en/papers/johnson-2022-automatic-dialect-density-estimation-african/) — Alexander Johnson, Kevin Everson, Vijay Ravi, et al. · English - [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. · Findings of the Association for Computational… - [BERT, can HE predict contrastive focus? Predicting and controlling prominence in neural TTS using a language model](https://md.research.elamur.ai/free/en/papers/stephenson-2022-bert-he-predict-contrastive-focus/) — Brooke Stephenson, Laurent Besacier, Laurent Girin, et al. · English - [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://md.research.elamur.ai/free/en/papers/grootendorst-2022-bertopic-neural-topic-modeling-class/) — Maarten Grootendorst · arXiv:2203.05794 - [Beyond neural scaling laws: beating power law scaling via data pruning](https://md.research.elamur.ai/free/en/papers/sorscher-2022-neural-scaling-laws-beating-power/) — Ben Sorscher, Robert Geirhos, Shashank Shekhar, et al. · arXiv:2206.14486 - [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/) — Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al. · arXiv:2206.04615 · Transactions on Machine… - [Binding Language Models in Symbolic Languages](https://md.research.elamur.ai/free/en/papers/cheng-2022-binding-language-models-symbolic-languages/) — Zhoujun Cheng, Tianbao Xie, Peng Shi, et al. · arXiv:2210.02875 - [BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage](https://md.research.elamur.ai/free/en/papers/shuster-2022-blenderbot-3-deployed-conversational-agent/) — Kurt Shuster, Jing Xu, Mojtaba Komeili, et al. · arXiv:2208.03188 - [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://md.research.elamur.ai/free/en/papers/li-2022-blip-bootstrapping-language-image-pre/) — Junnan Li, Dongxu Li, Caiming Xiong, et al. · arXiv:2201.12086 - [BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation](https://md.research.elamur.ai/free/en/papers/jiang-2022-blonde-automatic-evaluation-metric-document/) — Yuchen Jiang, Tianyu Liu, Shuming Ma, et al. · Proceedings of the 2022 Conference of the North… - [BRIO: Bringing Order to Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/liu-2022-brio-bringing-order-abstractive-summarization/) — Yixin Liu, Pengfei Liu, Dragomir Radev, et al. · Proceedings of the 60th Annual Meeting of the… - [BTPK-based interpretable method for NER tasks based on Talmudic Public Announcement Logic](https://md.research.elamur.ai/free/en/papers/langstream-2022-chen-btpk-based-interpretable-method-ner/) — Yulin Chen; Beishui Liao; Bruno Bentzen; Bo Yuan; Zelai Yao; Haixiao Chi;, Dov Gabbay · Aramaic - [Building a Manually Annotated Hungarian Coreference Corpus: Workflow and Tools](https://md.research.elamur.ai/free/en/papers/vadasz-2022-building-manually-annotated-hungarian-coreference/) — Noémi Vadász · Proceedings of the Fifth Workshop on… - [Camel Treebank: An Open Multi-genre Arabic Dependency Treebank](https://md.research.elamur.ai/free/en/papers/lingbulk-2022-habash-camel-treebank-open-multi-genre/) — Nizar Habash, Muhammed AbuOdeh, Dima Taji, et al. · Semitic languages - [Camelira: An Arabic Multi-Dialect Morphological Disambiguator](https://md.research.elamur.ai/free/en/papers/aclbulk-2022-obeid-camelira-arabic-multi-dialect-morphological/) — Ossama Obeid, Go Inoue, Nizar Habash · Semitic languages - [Can language models capture syntactic associations without surface cues? A case study of reflexive anaphor licensing in English control constructions](https://md.research.elamur.ai/free/en/papers/lee-2022-can-language-models-capture-syntactic/) — Soo-Hwan Lee, Sebastian Schuster · English - [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. · Findings of the Association for Computational… - [Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/dankers-2022-can-transformer-be-too-compositional/) — Verna Dankers, Christopher Lucas, Ivan Titov · Proceedings of the 60th Annual Meeting of the… - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/) — Jason Wei, Xuezhi Wang, Dale Schuurmans, et al. · arXiv:2201.11903 - [Challenges and Strategies in Cross-Cultural NLP](https://md.research.elamur.ai/free/en/papers/hershcovich-2022-challenges-strategies-cross-cultural-nlp/) — Daniel Hershcovich, Stella Frank, Heather Lent, et al. · Proceedings of the 60th Annual Meeting of the… - [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. · Findings of the Association for Computational… - [Classifier-Free Diffusion Guidance](https://md.research.elamur.ai/free/en/papers/ho-2022-classifier-free-diffusion-guidance/) — Jonathan Ho, Tim Salimans · arXiv:2207.12598 - [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://md.research.elamur.ai/free/en/papers/yu-2022-coca-contrastive-captioners-image-text/) — Jiahui Yu, Zirui Wang, Vijay Vasudevan, et al. · arXiv:2205.01917 - [CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/le-2022-coderl-mastering-code-generation-through/) — Hung Le, Yue Wang, Akhilesh Deepak Gotmare, et al. · arXiv:2207.01780 - [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://md.research.elamur.ai/free/en/papers/santhanam-2022-colbertv2-effective-efficient-retrieval-lightweight/) — Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, et al. · Proceedings of the 2022 Conference of the North… - [COMET-22: Unbabel-IST 2022 Submission for the Metrics Shared Task](https://md.research.elamur.ai/free/en/papers/rei-2022-comet-22-unbabel-ist-2022/) — Ricardo Rei, José G. C. de Souza, Duarte Alves, et al. · Proceedings of the Seventh Conference on… - [CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task](https://md.research.elamur.ai/free/en/papers/rei-2022-cometkiwi-ist-unbabel-2022-submission/) — Ricardo Rei, Marcos Treviso, Nuno M. Guerreiro, et al. · Proceedings of the Seventh Conference on… - [Comparing Native and Learner Englishes Using a Large Pre-trained Language Model](https://md.research.elamur.ai/free/en/papers/aoyama-2022-comparing-native-learner-englishes-large/) — Tatsuya Aoyama · Proceedings of the 11th Workshop on NLP for… - [Competition-Level Code Generation with AlphaCode](https://md.research.elamur.ai/free/en/papers/li-2022-competition-level-code-generation-alphacode/) — Yujia Li, David Choi, Junyoung Chung, et al. · arXiv:2203.07814 - [Complexity-Based Prompting for Multi-Step Reasoning](https://md.research.elamur.ai/free/en/papers/fu-2022-complexity-based-prompting-multi-step/) — Yao Fu, Hao Peng, Ashish Sabharwal, et al. · arXiv:2210.00720 - [ConnPrompt: Connective-cloze Prompt Learning for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/xiang-2022-connprompt-connective-cloze-prompt-learning/) — Wei Xiang, Zhenglin Wang, Lu Dai, et al. · Proceedings of the 29th International… - [Constitutional AI: Harmlessness from AI Feedback](https://md.research.elamur.ai/free/en/papers/bai-2022-constitutional-ai-harmlessness-ai-feedback/) — Yuntao Bai, Saurav Kadavath, Sandipan Kundu, et al. · arXiv:2212.08073 - [Context Matters: A Pragmatic Study of PLMs' Negation Understanding](https://md.research.elamur.ai/free/en/papers/gubelmann-2022-context-matters-pragmatic-study-plms/) — Reto Gubelmann, Siegfried Handschuh · Proceedings of the 60th Annual Meeting of the… - [Contrastive Decoding: Open-ended Text Generation as Optimization](https://md.research.elamur.ai/free/en/papers/li-2022-contrastive-decoding-open-ended-text/) — Xiang Lisa Li, Ari Holtzman, Daniel Fried, et al. · arXiv:2210.15097 - [Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities](https://md.research.elamur.ai/free/en/papers/hsu-2022-contrastive-representation-learning-cross-document/) — Benjamin Hsu, Graham Horwood · Proceedings of the 2022 Conference of the North… - [CorefUD 1.0: Coreference Meets Universal Dependencies](https://md.research.elamur.ai/free/en/papers/nedoluzhko-2022-corefud-1-0-coreference-meets/) — Anna Nedoluzhko, Michal Novák, Martin Popel, et al. · Mova's core list - [Cross-Dialect Social Media Dependency Parsing for Social Scientific Entity Attribute Analysis](https://md.research.elamur.ai/free/en/papers/eggleston-2022-cross-dialect-social-media-dependency/) — Chloe Eggleston, Brendan O'Connor · English - [Cross-document Event Coreference Search: Task, Dataset and Modeling](https://md.research.elamur.ai/free/en/papers/eirew-2022-cross-document-event-coreference-search/) — Alon Eirew, Avi Caciularu, Ido Dagan · Proceedings of the 2022 Conference on Empirical… - [Crosslingual Generalization through Multitask Finetuning](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-crosslingual-generalization-through-multitask-finetuning/) — Niklas Muennighoff, Thomas Wang, Lintang Sutawika, et al. · arXiv:2211.01786 - [CSECU-DSG @ Causal News Corpus 2022: Fusion of RoBERTa Transformers Variants for Causal Event Classification](https://md.research.elamur.ai/free/en/papers/aziz-2022-csecu-dsg-causal-news-corpus/) — Abdul Aziz, Md. Akram Hossain, Abu Nowshed Chy · Proceedings of the 5th Workshop on Challenges… - [Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations](https://md.research.elamur.ai/free/en/papers/davani-2022-dealing-disagreements-looking-beyond-majority/) — Aida Mostafazadeh Davani, Mark Díaz, Vinodkumar Prabhakaran · Transactions of the Association for… - [Deduplicating Training Data Mitigates Privacy Risks in Language Models](https://md.research.elamur.ai/free/en/papers/kandpal-2022-deduplicating-training-data-mitigates-privacy/) — Nikhil Kandpal, Eric Wallace, Colin Raffel · arXiv:2202.06539 - [DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale](https://md.research.elamur.ai/free/en/papers/rajbhandari-2022-deepspeed-moe-advancing-mixture-experts/) — Samyam Rajbhandari, Conglong Li, Zhewei Yao, et al. · arXiv:2201.05596 - [Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP](https://md.research.elamur.ai/free/en/papers/khattab-2022-demonstrate-search-predict-composing-retrieval/) — Omar Khattab, Keshav Santhanam, Xiang Lisa Li, et al. · arXiv:2212.14024 - [Design Choices in Crowdsourcing Discourse Relation Annotations: The Effect of Worker Selection and Training](https://md.research.elamur.ai/free/en/papers/scholman-2022-design-choices-crowdsourcing-discourse-relation/) — Merel Scholman, Valentina Pyatkin, Frances Yung, et al. · Proceedings of the Thirteenth Language… - [Detection of Negative Campaign in Israeli Municipal Elections](https://md.research.elamur.ai/free/en/papers/aclbulk-2022-litvak-detection-negative-campaign-israeli-municipal/) — Marina Litvak, Natalia Vanetik, Sagiv Talker, et al. · Modern Hebrew - [Diffusion-LM Improves Controllable Text Generation](https://md.research.elamur.ai/free/en/papers/li-2022-diffusion-lm-improves-controllable-text/) — Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, et al. · arXiv:2205.14217 - [DiscoGeM: A Crowdsourced Corpus of Genre-Mixed Implicit Discourse Relations](https://md.research.elamur.ai/free/en/papers/scholman-2022-discogem-crowdsourced-corpus-genre-mixed/) — Merel Scholman, Tianai Dong, Frances Yung, et al. · English - [Discovering Language Model Behaviors with Model-Written Evaluations](https://md.research.elamur.ai/free/en/papers/perez-2022-discovering-language-model-behaviors-model/) — Ethan Perez, Sam Ringer, Kamilė Lukošiūtė, et al. · arXiv:2212.09251 - [Disentangling Indirect Answers to Yes-No Questions in Real Conversations](https://md.research.elamur.ai/free/en/papers/sanagavarapu-2022-disentangling-indirect-answers-yes-no/) — Krishna Sanagavarapu, Jathin Singaraju, Anusha Kakileti, et al. · English - [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://md.research.elamur.ai/free/en/papers/ahn-2022-i-i-say-grounding-language/) — Michael Ahn, Anthony Brohan, Noah Brown, et al. · arXiv:2204.01691 - [Do Bashal and Hepsō really mean 'boil'? A preliminary study in the semantics of biblical Hebrew and Septuagint Greek](https://md.research.elamur.ai/free/en/papers/hbo-2022-mangum-bashal-hepso-really-mean-boil/) — Douglas T. Mangum · Ancient Hebrew - [Do Prompt-Based Models Really Understand the Meaning of Their Prompts?](https://md.research.elamur.ai/free/en/papers/webson-2022-do-prompt-based-models-really/) — Albert Webson, Ellie Pavlick · Proceedings of the 2022 Conference of the North… - [DocAMR: Multi-Sentence AMR Representation and Evaluation](https://md.research.elamur.ai/free/en/papers/naseem-2022-docamr-multi-sentence-amr-representation/) — Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, et al. · Machine translation - [Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs](https://md.research.elamur.ai/free/en/papers/jiang-2022-draft-sketch-prove-guiding-formal/) — Albert Q. 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