Papers

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Viewing 11-20 of 699 papers
  • Symbolic Knowledge Distillation: from General Language Models to Commonsense Models

    Peter West, Chandrasekhar Bhagavatula, Jack Hessel, Jena D. Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, S. Welleck, Yejin ChoiNAACL2022 The common practice for training commonsense models has gone from–human–to– corpus–to–machine: humans author commonsense knowledge graphs in order to train commonsense models. In this work, we investigate an alternative, from–machine–to–corpus– to–machine…
  • Time Waits for No One! Analysis and Challenges of Temporal Misalignment

    Kelvin Luu, Daniel Khashabi, Suchin Gururangan, Karishma Mandyam, Noah A. SmithNAACL2022 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. SmithNAACL2022 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…
  • Weakly Supervised Text-to-SQL Parsing through Question Decomposition

    Tomer Wolfson, Daniel Deutch, Jonathan BerantFindings of NAACL2022 Text-to-SQL parsers are crucial in enabling non-experts to effortlessly query relational data. Training such parsers, by contrast, generally requires expertise in annotating natural language (NL) utterances with corresponding SQL queries. In this work, we…
  • 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 MitchellFAccT2022 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…
  • DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models

    Gregor Betz, Kyle Richardson*SEM2022 In this paper, we present and implement a multi-dimensional, modular framework for performing deep argument analysis (DeepA2) using current pre-trained language models (PTLMs). ArgumentAnalyst – a T5 model (Raffel et al. 2020) set up and trained within DeepA2…
  • Correcting a coarse-grid climate model in multiple climates by machine learning from global 25-km resolution simulations

    Spencer K. Clark, Noah D. Brenowitz, Brian Henn, Anna Kwa, Jeremy McGibbon, W. Andre Perkins, Oliver Watt-Meyer, Christopher S. Bretherton, Lucas M. Harris Earth and Space Science Open Archive2022 Bretherton et al. (2022, https://doi.org/10.1029/2021MS002794) demonstrated a successful approach for using machine learning (ML) to help a coarse-resolution global atmosphere model with real geography (a ~200 km version of NOAA’s FV3GFS) evolve more like a…
  • Multimodal Knowledge Alignment with Reinforcement Learning

    Youngjae Yu, Jiwan Chung, Heeseung Yun, Jack Hessel, J. Park, Ximing Lu, Prithviraj Ammanabrolu, Rowan Zellers, Ronan Le Bras, Gunhee Kim, Yejin ChoiarXiv2022 Large language models readily adapt to novel settings, even without task-specific training data. Can their zero-shot capacity be extended to multimodal inputs? In this work, we propose ESPER which extends language-only zero-shot models to unseen multimodal…
  • NaturalProver: Grounded Mathematical Proof Generation with Language Models

    S. Welleck, Jiacheng Liu, Ximing Lu, Hannaneh Hajishirzi, Yejin ChoiarXiv2022 Theorem proving in natural mathematical language - the mixture of symbolic and natural language used by humans - plays a central role in mathematical advances and education, and tests aspects of reasoning that are core to intelligence. Yet it has remained…
  • ProsocialDialog: A Prosocial Backbone for Conversational Agents

    Hyunwoo Kim, Youngjae Yu, Liwei Jiang, Ximing Lu, Daniel Khashabi, Gunhee Kim, Yejin Choi, Maarten SaparXiv2022 Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them. To address this issue, we introduce ProsocialDialog, the first large-scale multi-turn dialogue dataset to teach…