--- title: "Natural language processing — papers, page 38" description: "Natural language processing: 2421 papers of the Elamur Research library · page 38 of 97 — our summary of each, its ideas, references and who cites it." canonical: "https://research.elamur.ai/free/en/shelves/nlp/page/38/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/nlp/page/38/) [home](https://md.research.elamur.ai/free/en/) / [shelves](https://md.research.elamur.ai/free/en/shelves/) / [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/) / page 38 # 💬 Natural language processing 2421 papers · page 38 of 97 · newest first · [all shelves](https://md.research.elamur.ai/free/en/shelves/) Computational linguistics from the ACL Anthology and arXiv: language models, information extraction, dialogue, evaluation. [← prev](https://md.research.elamur.ai/free/en/shelves/nlp/page/37/) [1](https://md.research.elamur.ai/free/en/shelves/nlp/) … [37](https://md.research.elamur.ai/free/en/shelves/nlp/page/37/) **38** [39](https://md.research.elamur.ai/free/en/shelves/nlp/page/39/) … [97](https://md.research.elamur.ai/free/en/shelves/nlp/page/97/) [next →](https://md.research.elamur.ai/free/en/shelves/nlp/page/39/) - [Dice Loss for Data-imbalanced NLP Tasks](https://md.research.elamur.ai/free/en/papers/li-2020-dice-loss-data-imbalanced-nlp/) — Xiaoya Li, Xiaofei Sun, Yuxian Meng, et al. · 2020 - [Distinguish Confusing Law Articles for Legal Judgment Prediction](https://md.research.elamur.ai/free/en/papers/xu-2020-distinguish-confusing-law-articles-legal/) — Nuo Xu, Pinghui Wang, Long Chen, et al. · 2020 - [Document Ranking with a Pretrained Sequence-to-Sequence Model](https://md.research.elamur.ai/free/en/papers/nogueira-2020-document-ranking-pretrained-sequence-sequence/) — Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, et al. · 2020 - [Does Syntax Need to Grow on Trees? Sources of Hierarchical Inductive Bias in Sequence-to-Sequence Networks](https://md.research.elamur.ai/free/en/papers/mccoy-2020-does-syntax-need-grow-trees/) — R. Thomas McCoy, Robert Frank, Tal Linzen · 2020 - [Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training](https://md.research.elamur.ai/free/en/papers/li-2020-don-t-say-that-making/) — Margaret Li, Stephen Roller, Ilia Kulikov, et al. · 2020 - [Efficient Dialogue State Tracking by Selectively Overwriting Memory](https://md.research.elamur.ai/free/en/papers/kim-2020-efficient-dialogue-state-tracking-selectively/) — Sungdong Kim, Sohee Yang, Gyuwan Kim, et al. · 2020 - [Efficient Intent Detection with Dual Sentence Encoders](https://md.research.elamur.ai/free/en/papers/casanueva-2020-efficient-intent-detection-dual-sentence/) — Iñigo Casanueva, Tadas Temčinas, Daniela Gerz, et al. · 2020 - [Efficient Second-Order TreeCRF for Neural Dependency Parsing](https://md.research.elamur.ai/free/en/papers/acl-2020-zhang-efficient-second-order-treecrf-neural/) — Yu Zhang, Zhenghua Li, Min Zhang · 2020 - [Emerging Cross-lingual Structure in Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/conneau-2020-emerging-cross-lingual-structure-pretrained/) — Alexis Conneau, Shijie Wu, Haoran Li, et al. · 2020 - [Enabling Language Models to Fill in the Blanks](https://md.research.elamur.ai/free/en/papers/donahue-2020-enabling-language-models-fill-blanks/) — Chris Donahue, Mina Lee, Percy Liang · 2020 - [End-to-End Bias Mitigation by Modelling Biases in Corpora](https://md.research.elamur.ai/free/en/papers/mahabadi-2020-end-end-bias-mitigation-modelling/) — Rabeeh Karimi Mahabadi, Yonatan Belinkov, James Henderson · 2020 - [End-to-End Slot Alignment and Recognition for Cross-Lingual NLU](https://md.research.elamur.ai/free/en/papers/xu-2020-end-end-slot-alignment-recognition/) — Weijia Xu, Batool Haider, Saab Mansour · 2020 - [Entities as Experts: Sparse Memory Access with Entity Supervision](https://md.research.elamur.ai/free/en/papers/fevry-2020-entities-as-experts-sparse-memory/) — Thibault Févry, Livio Baldini Soares, Nicholas FitzGerald, et al. · 2020 - [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://md.research.elamur.ai/free/en/papers/deyoung-2020-eraser-benchmark-evaluate-rationalized-nlp/) — Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, et al. · 2020 - [ETC: Encoding Long and Structured Inputs in Transformers](https://md.research.elamur.ai/free/en/papers/ainslie-2020-etc-encoding-long-structured-inputs/) — Joshua Ainslie, Santiago Ontañón, Chris Alberti, et al. · 2020 - [Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples](https://md.research.elamur.ai/free/en/papers/zheng-2020-evaluating-enhancing-robustness-neural-network/) — Xiaoqing Zheng, Jiehang Zeng, Yi Zhou, et al. · 2020 - [Evaluating Factuality in Generation with Dependency-level Entailment](https://md.research.elamur.ai/free/en/papers/goyal-2020-evaluating-factuality-generation-dependency-level/) — Tanya Goyal, Greg Durrett · 2020 - [Evaluating Word Embeddings on Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/aclbulk-2020-stringham-evaluating-word-embeddings-low-resource/) — Nathan Stringham, Mike Izbicki · 2020 - [Event Extraction as Machine Reading Comprehension](https://md.research.elamur.ai/free/en/papers/liu-2020-event-extraction-as-machine-reading/) — Jian Liu, Yubo Chen, Kang Liu, et al. · 2020 - [Event Extraction as Multi-turn Question Answering](https://md.research.elamur.ai/free/en/papers/li-2020-event-extraction-as-multi-turn/) — Fayuan Li, Weihua Peng, Yuguang Chen, et al. · 2020 - [Event Extraction by Answering (Almost) Natural Questions](https://md.research.elamur.ai/free/en/papers/du-2020-event-extraction-answering-almost-natural/) — Xinya Du, Claire Cardie · 2020 - [Experience Grounds Language](https://md.research.elamur.ai/free/en/papers/bisk-2020-experience-grounds-language/) — Yonatan Bisk, Ari Holtzman, Jesse Thomason, et al. · 2020 - [Explainable Automated Fact-Checking for Public Health Claims](https://md.research.elamur.ai/free/en/papers/kotonya-2020-explainable-automated-fact-checking-public/) — Neema Kotonya, Francesca Toni · 2020 - [Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions](https://md.research.elamur.ai/free/en/papers/han-2020-explaining-black-box-predictions-unveiling/) — Xiaochuang Han, Byron C. Wallace, Yulia Tsvetkov · 2020 - [Exploring and Predicting Transferability across NLP Tasks](https://md.research.elamur.ai/free/en/papers/vu-2020-exploring-predicting-transferability-across-nlp/) — Tu Vu, Tong Wang, Tsendsuren Munkhdalai, et al. · 2020 [← prev](https://md.research.elamur.ai/free/en/shelves/nlp/page/37/) [1](https://md.research.elamur.ai/free/en/shelves/nlp/) … [37](https://md.research.elamur.ai/free/en/shelves/nlp/page/37/) **38** [39](https://md.research.elamur.ai/free/en/shelves/nlp/page/39/) … [97](https://md.research.elamur.ai/free/en/shelves/nlp/page/97/) [next →](https://md.research.elamur.ai/free/en/shelves/nlp/page/39/)