--- title: "Natural language processing — papers, page 33" description: "Natural language processing: 2421 papers of the Elamur Research library · page 33 of 97 — our summary of each, its ideas, references and who cites it." canonical: "https://research.elamur.ai/free/en/shelves/nlp/page/33/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/nlp/page/33/) [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 33 # 💬 Natural language processing 2421 papers · page 33 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/32/) [1](https://md.research.elamur.ai/free/en/shelves/nlp/) … [32](https://md.research.elamur.ai/free/en/shelves/nlp/page/32/) **33** [34](https://md.research.elamur.ai/free/en/shelves/nlp/page/34/) … [97](https://md.research.elamur.ai/free/en/shelves/nlp/page/97/) [next →](https://md.research.elamur.ai/free/en/shelves/nlp/page/34/) - [Multilingual Translation from Denoising Pre-Training](https://md.research.elamur.ai/free/en/papers/tang-2021-multilingual-translation-denoising-pre-training/) — Yuqing Tang, Chau Tran, Xian Li, et al. · 2021 - [Muppet: Massive Multi-task Representations with Pre-Finetuning](https://md.research.elamur.ai/free/en/papers/aghajanyan-2021-muppet-massive-multi-task-representations/) — Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava, et al. · 2021 - [Neural Modeling for Named Entities and Morphology (NEMO2)](https://md.research.elamur.ai/free/en/papers/tacl-2021-bareket-neural-modeling-named-entities-morphology/) — Dan Bareket, Reut Tsarfaty · 2021 - [NLP in the DH pipeline: Transfer-learning to a Chronolect](https://md.research.elamur.ai/free/en/papers/aclbulk-2021-rubinstein-nlp-dh-pipeline-transfer-learning/) — Aynat Rubinstein, Avi Shmidman · 2021 - [On Generative Spoken Language Modeling from Raw Audio](https://md.research.elamur.ai/free/en/papers/lakhotia-2021-generative-spoken-language-modeling-raw/) — Kushal Lakhotia, Eugene Kharitonov, Wei-Ning Hsu, et al. · 2021 - [On Hallucination and Predictive Uncertainty in Conditional Language Generation](https://md.research.elamur.ai/free/en/papers/xiao-2021-hallucination-predictive-uncertainty-conditional-language/) — Yijun Xiao, William Yang Wang · 2021 - [On Releasing Annotator-Level Labels and Information in Datasets](https://md.research.elamur.ai/free/en/papers/prabhakaran-2021-releasing-annotator-level-labels-information/) — Vinodkumar Prabhakaran, Aida Mostafazadeh Davani, Mark Diaz · 2021 - [On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation](https://md.research.elamur.ai/free/en/papers/he-2021-effectiveness-adapter-based-tuning-pretrained/) — Ruidan He, Linlin Liu, Hai Ye, et al. · 2021 - [ONION: A Simple and Effective Defense Against Textual Backdoor Attacks](https://md.research.elamur.ai/free/en/papers/qi-2021-onion-simple-effective-defense-against/) — Fanchao Qi, Yangyi Chen, Mukai Li, et al. · 2021 - [Open-Domain Question Answering Goes Conversational via Question Rewriting](https://md.research.elamur.ai/free/en/papers/anantha-2021-open-domain-question-answering-goes/) — Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, et al. · 2021 - [PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation](https://md.research.elamur.ai/free/en/papers/zeng-2021-pangu-alpha-large-scale/) — Wei Zeng, Xiaozhe Ren, Teng Su, et al. · 2021 - [PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them](https://md.research.elamur.ai/free/en/papers/lewis-2021-paq-65-million-probably-asked/) — Patrick Lewis, Yuxiang Wu, Linqing Liu, et al. · 2021 - [Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases](https://md.research.elamur.ai/free/en/papers/chalkidis-2021-paragraph-level-rationale-extraction-through/) — Ilias Chalkidis, Manos Fergadiotis, Dimitrios Tsarapatsanis, et al. · 2021 - [Paragraph-level Simplification of Medical Texts](https://md.research.elamur.ai/free/en/papers/devaraj-2021-paragraph-level-simplification-medical-texts/) — Ashwin Devaraj, Iain Marshall, Byron Wallace, et al. · 2021 - [Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks](https://md.research.elamur.ai/free/en/papers/mahabadi-2021-parameter-efficient-multi-task-fine/) — Rabeeh Karimi Mahabadi, Sebastian Ruder, Mostafa Dehghani, et al. · 2021 - [Parameter-Efficient Transfer Learning with Diff Pruning](https://md.research.elamur.ai/free/en/papers/guo-2021-parameter-efficient-transfer-learning-diff/) — Demi Guo, Alexander Rush, Yoon Kim · 2021 - [Planning with Learned Entity Prompts for Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/narayan-2021-planning-learned-entity-prompts-abstractive/) — Shashi Narayan, Yao Zhao, Joshua Maynez, et al. · 2021 - [Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models](https://md.research.elamur.ai/free/en/papers/wu-2021-polyjuice-generating-counterfactuals-explaining-evaluating/) — Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, et al. · 2021 - [Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence](https://md.research.elamur.ai/free/en/papers/bianchi-2021-pre-training-is-hot-topic/) — Federico Bianchi, Silvia Terragni, Dirk Hovy · 2021 - [ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language](https://md.research.elamur.ai/free/en/papers/tafjord-2021-proofwriter-generating-implications-proofs-abductive/) — Oyvind Tafjord, Bhavana Dalvi, Peter Clark · 2021 - [QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://md.research.elamur.ai/free/en/papers/zhong-2021-qmsum-new-benchmark-query-based/) — Ming Zhong, Da Yin, Tao Yu, et al. · 2021 - [Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets](https://md.research.elamur.ai/free/en/papers/kreutzer-2021-quality-glance-audit-web-crawled/) — Julia Kreutzer, Isaac Caswell, Lisa Wang, et al. · 2021 - [QuestEval: Summarization Asks for Fact-based Evaluation](https://md.research.elamur.ai/free/en/papers/scialom-2021-questeval-summarization-asks-fact-based/) — Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, et al. · 2021 - [Q²: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering](https://md.research.elamur.ai/free/en/papers/honovich-2021-q-2-evaluating-factual-consistency/) — Or Honovich, Leshem Choshen, Roee Aharoni, et al. · 2021 - [Recipes for Building an Open-Domain Chatbot](https://md.research.elamur.ai/free/en/papers/roller-2021-recipes-building-open-domain-chatbot/) — Stephen Roller, Emily Dinan, Naman Goyal, et al. · 2021 [← prev](https://md.research.elamur.ai/free/en/shelves/nlp/page/32/) [1](https://md.research.elamur.ai/free/en/shelves/nlp/) … [32](https://md.research.elamur.ai/free/en/shelves/nlp/page/32/) **33** [34](https://md.research.elamur.ai/free/en/shelves/nlp/page/34/) … [97](https://md.research.elamur.ai/free/en/shelves/nlp/page/97/) [next →](https://md.research.elamur.ai/free/en/shelves/nlp/page/34/)