---
title: "Papers of 2022 — page 4"
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/papers/2022/page/4/"
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# Papers of 2022 — page 4

343 papers · [all years](https://md.research.elamur.ai/free/papers/)

[1](https://md.research.elamur.ai/free/papers/2022/) [2](https://md.research.elamur.ai/free/papers/2022/page/2/) [3](https://md.research.elamur.ai/free/papers/2022/page/3/) **4**

- [The Universal Anaphora Scorer](https://md.research.elamur.ai/free/papers/yu-2022-universal-anaphora-scorer/) — Juntao Yu, Sopan Khosla, Nafise Sadat Moosavi, et al. · Proceedings of the Thirteenth Language…
- [The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning](https://md.research.elamur.ai/free/papers/ye-2022-unreliability-explanations-few-shot-prompting/) — Xi Ye, Greg Durrett · arXiv:2205.03401
- [The “Problem” of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation](https://md.research.elamur.ai/free/papers/plank-2022-problem-human-label-variation-ground/) — Barbara Plank · Proceedings of the 2022 Conference on Empirical…
- [Towards a Cleaner Document-Oriented Multilingual Crawled Corpus](https://md.research.elamur.ai/free/papers/abadji-2022-towards-cleaner-document-oriented-multilingual/) — Julien Abadji, Pedro Ortiz Suarez, Laurent Romary, et al. · arXiv:2201.06642
- [Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement](https://md.research.elamur.ai/free/papers/liu-2022-multi-modal-sarcasm-detection-hierarchical/) — Hui Liu, Wenya Wang, Haoliang Li · Proceedings of the 2022 Conference on Empirical…
- [Towards Pragmatic Production Strategies for Natural Language Generation Tasks](https://md.research.elamur.ai/free/papers/giulianelli-2022-pragmatic-production-strategies-natural-language/) — Mario Giulianelli · English
- [Towards the Construction of a WordNet for Old English](https://md.research.elamur.ai/free/papers/khan-2022-construction-wordnet-old-english/) — Anas Fahad Khan, Francisco J. Minaya Gómez, Rafael Cruz González, et al. · English
- [Towards Universal Segmentations: UniSegments 1.0](https://md.research.elamur.ai/free/papers/abokrtsky-2022-universal-segmentations-unisegments-1-0/) — Zdeněk Žabokrtský, Niyati Bafna, Jan Bodnár, et al. · Proceedings of the Thirteenth Language…
- [ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection](https://md.research.elamur.ai/free/papers/hartvigsen-2022-toxigen-large-scale-machine-generated/) — Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, et al. · arXiv:2203.09509
- [Tracing Syntactic Change in the Scientific Genre: Two Universal Dependency-parsed Diachronic Corpora of Scientific English and German](https://md.research.elamur.ai/free/papers/ud-2022-krielke-tracing-syntactic-change-scientific-genre/) — Marie-Pauline Krielke, Luigi Talamo, Mahmoud Fawzi, et al. · English
- [Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space](https://md.research.elamur.ai/free/papers/geva-2022-transformer-feed-forward-layers-build/) — Mor Geva, Avi Caciularu, Kevin Ro Wang, et al. · arXiv:2203.14680
- [Transformer Memory as a Differentiable Search Index](https://md.research.elamur.ai/free/papers/tay-2022-transformer-memory-differentiable-search-index/) — Yi Tay, Vinh Q. Tran, Mostafa Dehghani, et al. · arXiv:2202.06991
- [Transformers learn in-context by gradient descent](https://md.research.elamur.ai/free/papers/oswald-2022-transformers-learn-context-gradient-descent/) — Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, et al. · arXiv:2212.07677
- [TRUE: Re-evaluating Factual Consistency Evaluation](https://md.research.elamur.ai/free/papers/honovich-2022-true-re-evaluating-factual-consistency/) — Or Honovich, Roee Aharoni, Jonathan Herzig, et al. · Proceedings of the 2022 Conference of the North…
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://md.research.elamur.ai/free/papers/lin-2022-truthfulqa-measuring-how-models-mimic/) — Stephanie Lin, Jacob Hilton, Owain Evans · Proceedings of the 60th Annual Meeting of the…
- [Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks](https://md.research.elamur.ai/free/papers/rottger-2022-two-contrasting-data-annotation-paradigms/) — Paul Röttger, Bertie Vidgen, Dirk Hovy, et al. · Proceedings of the 2022 Conference of the North…
- [TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages](https://md.research.elamur.ai/free/papers/srinivasan-2022-tydip-dataset-politeness-classification-nine/) — Anirudh Srinivasan, Eunsol Choi · Findings of the Association for Computational…
- [UL2: Unifying Language Learning Paradigms](https://md.research.elamur.ai/free/papers/tay-2022-ul2-unifying-language-learning-paradigms/) — Yi Tay, Mostafa Dehghani, Vinh Q. Tran, et al. · arXiv:2205.05131
- [UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models](https://md.research.elamur.ai/free/papers/xie-2022-unifiedskg-unifying-multi-tasking-structured/) — Tianbao Xie, Chen Henry Wu, Peng Shi, et al. · Proceedings of the 2022 Conference on Empirical…
- [UniMorph 4.0: Universal Morphology](https://md.research.elamur.ai/free/papers/lex-2022-batsuren-unimorph-4/) — Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa · English
- [UniTE: Unified Translation Evaluation](https://md.research.elamur.ai/free/papers/wan-2022-unite-unified-translation-evaluation/) — Yu Wan, Dayiheng Liu, Baosong Yang, et al. · Proceedings of the 60th Annual Meeting of the…
- [Universal Dependencies and Semantics for English and Hebrew Child-directed Speech](https://md.research.elamur.ai/free/papers/ud-2022-szubert-universal-dependencies-semantics-english-hebrew/) — Ida Szubert, Omri Abend, Nathan Schneider, et al. · English
- [Universal Dependencies for Punjabi](https://md.research.elamur.ai/free/papers/arora-2022-universal-dependencies-punjabi/) — Aryaman Arora · Proceedings of the Thirteenth Language…
- [Universal Grammatical Dependencies for Portuguese with CINTIL Data, LX Processing and CLARIN support](https://md.research.elamur.ai/free/papers/branco-2022-universal-grammatical-dependencies-portuguese-cintil/) — António Branco, João Ricardo Silva, Luís Gomes, et al. · Proceedings of the Thirteenth Language…
- [UniXcoder: Unified Cross-Modal Pre-training for Code Representation](https://md.research.elamur.ai/free/papers/guo-2022-unixcoder-unified-cross-modal-pre/) — Daya Guo, Shuai Lu, Nan Duan, et al. · Proceedings of the 60th Annual Meeting of the…
- [Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval](https://md.research.elamur.ai/free/papers/gao-2022-unsupervised-corpus-aware-language-model/) — Luyu Gao, Jamie Callan · Proceedings of the 60th Annual Meeting of the…
- [Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations](https://md.research.elamur.ai/free/papers/huang-2022-unsupervised-syntactically-controlled-paraphrase-generation/) — Kuan-Hao Huang, Varun Iyer, Anoop Kumar, et al. · Machine translation
- [Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](https://md.research.elamur.ai/free/papers/smith-2022-using-deepspeed-megatron-train-megatron/) — Shaden Smith, Mostofa Patwary, Brandon Norick, et al. · arXiv:2201.11990
- [VALUE: Understanding Dialect Disparity in NLU](https://md.research.elamur.ai/free/papers/ziems-2022-value-understanding-dialect-disparity-nlu/) — Caleb Ziems, Jiaao Chen, Camille Harris, et al. · English
- [WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation](https://md.research.elamur.ai/free/papers/liu-2022-wanli-worker-ai-collaboration-natural/) — Alisa Liu, Swabha Swayamdipta, Noah A. Smith, et al. · arXiv:2201.05955
- [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://md.research.elamur.ai/free/papers/yao-2022-webshop-towards-scalable-real-world/) — Shunyu Yao, Howard Chen, John Yang, et al. · arXiv:2207.01206
- [What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural text](https://md.research.elamur.ai/free/papers/fang-2022-what-does-it-take-bake/) — Biaoyan Fang, Timothy Baldwin, Karin Verspoor · Findings of the Association for Computational…
- [What learning algorithm is in-context learning? Investigations with linear models](https://md.research.elamur.ai/free/papers/akyurek-2022-learning-algorithm-context-learning-investigations/) — Ekin Akyürek, Dale Schuurmans, Jacob Andreas, et al. · arXiv:2211.15661
- [What Makes Good In-Context Examples for GPT-3?](https://md.research.elamur.ai/free/papers/liu-2022-what-makes-good-context-examples/) — Jiachang Liu, Dinghan Shen, Yizhe Zhang, et al. · Proceedings of Deep Learning Inside Out…
- [When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues](https://md.research.elamur.ai/free/papers/kumar-2022-when-did-you-become-so/) — Shivani Kumar, Atharva Kulkarni, Md Shad Akhtar, et al. · Proceedings of the 60th Annual Meeting of the…
- [Word-Embeddings Distinguish Denominal and Root-Derived Verbs in Semitic](https://md.research.elamur.ai/free/papers/hbo-2022-benbaji-word-embeddings-distinguish-denominal-root/) — Ido Benbaji, Omri Doron, Adele Henot-Mortier · Ancient Hebrew
- [XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond](https://md.research.elamur.ai/free/papers/barbieri-2022-xlm-t-multilingual-language-models/) — Francesco Barbieri, Luis Espinosa Anke, Jose Camacho-Collados · Proceedings of the Thirteenth Language…
- [ZeroGen: Efficient Zero-shot Learning via Dataset Generation](https://md.research.elamur.ai/free/papers/ye-2022-zerogen-efficient-zero-shot-learning/) — Jiacheng Ye, Jiahui Gao, Qintong Li, et al. · Proceedings of the 2022 Conference on Empirical…
- [ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers](https://md.research.elamur.ai/free/papers/yao-2022-zeroquant-efficient-affordable-post-training/) — Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, et al. · arXiv:2206.01861
- [ÚFAL CorPipe at CRAC 2022: Effectivity of Multilingual Models for Coreference Resolution](https://md.research.elamur.ai/free/papers/straka-2022-fal-corpipe-crac-2022-effectivity/) — Milan Straka, Jana Straková · Proceedings of the CRAC 2022 Shared Task on…
- [♫ MuSiQue: Multihop Questions via Single-hop Question Composition](https://md.research.elamur.ai/free/papers/trivedi-2022-musique-multihop-questions-single-hop/) — Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, et al. · Transactions of the Association for…
- [深度知识追踪模型综述和性能比较](https://md.research.elamur.ai/free/papers/wang-2022-performance-comparison-deep/) — 王宇, 朱梦霞, 杨尚辉, et al. · 软件学报 (Journal of Software)
- [视觉语言预训练综述](https://md.research.elamur.ai/free/papers/yin-2022-vision-language-pre/) — 殷炯, 张哲东, 高宇涵, et al. · 软件学报 (Journal of Software)

[1](https://md.research.elamur.ai/free/papers/2022/) [2](https://md.research.elamur.ai/free/papers/2022/page/2/) [3](https://md.research.elamur.ai/free/papers/2022/page/3/) **4**