--- title: "Natural language processing — papers, page 32" description: "Natural language processing: 2421 papers of the Elamur Research library · page 32 of 97 — our summary of each, its ideas, references and who cites it." canonical: "https://research.elamur.ai/free/en/shelves/nlp/page/32/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/nlp/page/32/) [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 32 # 💬 Natural language processing 2421 papers · page 32 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/31/) [1](https://md.research.elamur.ai/free/en/shelves/nlp/) … [31](https://md.research.elamur.ai/free/en/shelves/nlp/page/31/) **32** [33](https://md.research.elamur.ai/free/en/shelves/nlp/page/33/) … [97](https://md.research.elamur.ai/free/en/shelves/nlp/page/97/) [next →](https://md.research.elamur.ai/free/en/shelves/nlp/page/33/) - [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 · 2021 - [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. · 2021 - [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. · 2021 - [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. · 2021 - [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. · 2021 - [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. · 2021 - [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 · 2021 - [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. · 2021 - [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. · 2021 - [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. · 2021 - [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. · 2021 - [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. · 2021 - [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 · 2021 - [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 · 2021 - [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 · 2021 - [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. · 2021 - [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. · 2021 - [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. · 2021 - [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 · 2021 - [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. · 2021 - [Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer](https://md.research.elamur.ai/free/en/papers/qi-2021-mind-style-text-adversarial-backdoor/) — Fanchao Qi, Yangyi Chen, Xurui Zhang, et al. · 2021 - [MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets](https://md.research.elamur.ai/free/en/papers/pramanick-2021-momenta-multimodal-framework-detecting-harmful/) — Shraman Pramanick, Shivam Sharma, Dimitar Dimitrov, et al. · 2021 - [MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark](https://md.research.elamur.ai/free/en/papers/li-2021-mtop-comprehensive-multilingual-task-oriented/) — Haoran Li, Abhinav Arora, Shuohui Chen, et al. · 2021 - [MultiEURLEX - A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer](https://md.research.elamur.ai/free/en/papers/chalkidis-2021-multieurlex-multi-lingual-multi-label/) — Ilias Chalkidis, Manos Fergadiotis, Ion Androutsopoulos · 2021 - [Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/kassner-2021-multilingual-lama-investigating-knowledge-multilingual/) — Nora Kassner, Philipp Dufter, Hinrich Schütze · 2021 [← prev](https://md.research.elamur.ai/free/en/shelves/nlp/page/31/) [1](https://md.research.elamur.ai/free/en/shelves/nlp/) … [31](https://md.research.elamur.ai/free/en/shelves/nlp/page/31/) **32** [33](https://md.research.elamur.ai/free/en/shelves/nlp/page/33/) … [97](https://md.research.elamur.ai/free/en/shelves/nlp/page/97/) [next →](https://md.research.elamur.ai/free/en/shelves/nlp/page/33/)