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Viewing 111-120 of 292 papers
Few-Shot Self-Rationalization with Natural Language Prompts
Ana Marasović, Iz Beltagy, Doug Downey, Matthew E. PetersFindings of NAACL • 2022 Self-rationalization models that predict task labels and generate free-text elaborations for their predictions could enable more intuitive interaction with NLP systems. These models are, however, currently trained with a large amount of human-written free…MultiVerS: Improving scientific claim verification with weak supervision and full-document context
David Wadden, Kyle Lo, Lucy Lu Wang, Arman Cohan, Iz Beltagy, Hannaneh HajishirziFindings of NAACL • 2022 The scientific claim verification task requires an NLP system to label scientific documents which Support or Refute an input claim, and to select evidentiary sentences (or rationales) justifying each predicted label. In this work, we present MultiVerS, which…NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics
Ximing Lu, S. Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, Noah A. Smith, Yejin ChoiNAACL • 2022The dominant paradigm for neural text generation is left-to-right decoding from autoregressive language models. Constrained or controllable generation under complex lexical constraints, however, requires foresight to plan ahead feasible future paths. Drawing…Best Paper AwardTime Waits for No One! Analysis and Challenges of Temporal Misalignment
Kelvin Luu, Daniel Khashabi, Suchin Gururangan, Karishma Mandyam, Noah A. SmithNAACL • 2022 When an NLP model is trained on text data from one time period and tested or deployed on data from another, the resulting temporal misalignment can degrade end-task performance. In this work, we establish a suite of eight diverse tasks across different…Transparent Human Evaluation for Image Captioning
Jungo Kasai, Keisuke Sakaguchi, Lavinia Dunagan, Jacob Morrison, Ronan Le Bras, Yejin Choi, Noah A. SmithNAACL • 2022 We establish a rubric-based human evaluation protocol for image captioning models. Our scoring rubrics and their definitions are carefully developed based on machineand humangenerated captions on the MSCOCO dataset. Each caption is evaluated along two main…Data Governance in the Age of Large-Scale Data-Driven Language Technology
Yacine Jernite, Huu Nguyen, Stella Rose Biderman, A. Rogers, Maraim Masoud, V. Danchev, Samson Tan, A. Luccioni, Nishant Subramani, Gérard Dupont, Jesse Dodge, Kyle Lo, Zeerak Talat, Isaac Johnson, Dragomir R. Radev, Somaieh Nikpoor, Jorg Frohberg, Aaron Gokaslan, Peter Henderson, Rishi Bommasani, Margaret MitchellFAccT • 2022 The recent emergence and adoption of Machine Learning technology, and specifically of Large Language Models, has drawn attention to the need for systematic and transparent management of language data. This work proposes an approach to global language data…Measuring the Carbon Intensity of AI in Cloud Instances
Jesse Dodge, Taylor Prewitt, Rémi Tachet des Combes, Erika Odmark, Roy Schwartz, Emma Strubell, A. Luccioni, Noah A. Smith, Nicole DeCario, Will BuchananFAccT • 2022 The advent of cloud computing has provided people around the world with unprecedented access to computational power and enabled rapid growth in technologies such as machine learning, the computational demands of which incur a high energy cost and a…Domain Mismatch Doesn’t Always Prevent Cross-Lingual Transfer Learning
Daniel Edmiston, Phillip Keung, Noah A. SmithLREC • 2022 Cross-lingual transfer learning without labeled target language data or parallel text has been surprisingly effective in zero-shot cross-lingual classification, question answering, unsupervised machine translation, etc. However, some recent publications have…What Language Model to Train if You Have One Million GPU Hours?
Teven Le Scao, Thomas Wang, Daniel Hesslow, Lucile Saulnier, Stas Bekman, Saiful Bari, Stella Rose Biderman, Hady ElSahar, Jason Phang, Ofir Press, Colin Raffel, Victor Sanh, Sheng Shen, Lintang A. Sutawika, Jaesung Tae, Zheng Xin Yong, Julien Launay, Iz BeltagyEMNLP • 2022 The crystallization of modeling methods around the Transformer architecture has been a boon for practitioners. Simple, well-motivated architectural variations that transfer across tasks and scale, increasing the impact of modeling research. However, with the…Retrieval Data Augmentation Informed by Downstream Question Answering Performance
James Ferguson, Pradeep Dasigi, Tushar Khot, Hannaneh HajishirziACL • FEVER • 2022 Training retrieval models to fetch contexts for Question Answering (QA) over large corpora requires labeling relevant passages in those corpora. Since obtaining exhaustive manual annotations of all relevant passages is not feasible, prior work uses text…