Award Winning Papers

Learn more about AI2's Lasting Impact Award
Viewing 11-20 of 33 papers
  • Specializing Multilingual Language Models: An Empirical Study

    Ethan C. Chau, Noah A. SmithEMNLP • Workshop on Multilingual Representation Learning2021
    Best Paper Honorable Mention
    Pretrained multilingual language models have become a common tool in transferring NLP capabilities to low-resource languages, often with adaptations. In this work, we study the performance, extensibility, and interaction of two such adaptations: vocabulary…
  • SciA11y: Converting Scientific Papers to Accessible HTML

    Lucy Lu Wang, Isabel Cachola, Jonathan Bragg, Evie (Yu-Yen) Cheng, Chelsea Hess Haupt, Matt Latzke, Bailey Kuehl, Madeleine van Zuylen, Linda M. Wagner, Daniel S. WeldASSETS2021
    Best Artifact Award
    We present SciA11y, a system that renders inaccessible scientific paper PDFs into HTML. SciA11y uses machine learning models to extract and understand the content of scientific PDFs, and reorganizes the resulting paper components into a form that better…
  • SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts

    Arie Cattan, Sophie Johnson, Daniel S. Weld, Ido Dagan, Iz Beltagy, Doug Downey, Tom HopeAKBC2021 Determining coreference of concept mentions across multiple documents is fundamental for natural language understanding. Work on cross-document coreference resolution (CDCR) typically considers mentions of events in the news, which do not often involve…
  • All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text

    Elizabeth Clark, Tal August, Sofia Serrano, Nikita Haduong, Suchin Gururangan, Noah A. SmithACL2021 Human evaluations are typically considered the gold standard in natural language generation, but as models' fluency improves, how well can evaluators detect and judge machine-generated text? We run a study assessing non-experts' ability to distinguish between…
  • From 'F' to 'A' on the N.Y. Regents Science Exams: An Overview of the Aristo Project

    Peter Clark, Oren Etzioni, Daniel Khashabi, Tushar Khot, Bhavana Dalvi Mishra, Kyle Richardson, Ashish Sabharwal, Carissa Schoenick, Oyvind Tafjord, Niket Tandon, Sumithra Bhakthavatsalam, Dirk Groeneveld, Michal Guerquin, Michael SchmitzAI Magazine2020
    AI2 Lasting Impact Award
    AI has achieved remarkable mastery over games such as Chess, Go, and Poker, and even Jeopardy!, but the rich variety of standardized exams has remained a landmark challenge. Even in 2016, the best AI system achieved merely 59.3% on an 8th Grade science exam…
  • Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks

    Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, Noah A. SmithACL2020 Language models pretrained on text from a wide variety of sources form the foundation of today's NLP. In light of the success of these broad-coverage models, we investigate whether it is still helpful to tailor a pretrained model to the domain of a target…
  • Social Bias Frames: Reasoning about Social and Power Implications of Language

    Maarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, Yejin ChoiACL2020
    WeCNLP Best Paper
    Language has the power to reinforce stereotypes and project social biases onto others. At the core of the challenge is that it is rarely what is stated explicitly, but all the implied meanings that frame people's judgements about others. For example, given a…
  • Procedural Reading Comprehension with Attribute-Aware Context Flow

    Aida Amini, Antoine Bosselut, Bhavana Dalvi Mishra, Yejin Choi, Hannaneh HajishirziAKBC2020 Procedural texts often describe processes (e.g., photosynthesis and cooking) that happen over entities (e.g., light, food). In this paper, we introduce an algorithm for procedural reading comprehension by translating the text into a general formalism that…
  • WinoGrande: An Adversarial Winograd Schema Challenge at Scale

    Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, Yejin ChoiAAAI2020 The Winograd Schema Challenge (WSC), proposed by Levesque et al. (2011) as an alternative to the Turing Test, was originally designed as a pronoun resolution problem that cannot be solved based on statistical patterns in large text corpora. However, recent…
  • Evaluating Question Answering Evaluation

    Anthony Chen, Gabriel Stanovsky, Sameer Singh, Matt GardnerEMNLP • MRQA Workshop2019 As the complexity of question answering (QA) datasets evolve, moving away from restricted formats like span extraction and multiple-choice (MC) to free-form answer generation, it is imperative to understand how well current metrics perform in evaluating QA…