Papers
See AI2's Award Winning Papers
Learn more about AI2's Lasting Impact Award
Viewing 181-190 of 292 papers
XOR QA: Cross-lingual Open-Retrieval Question Answering
Akari Asai, Jungo Kasai, J. Clark, Kenton Lee, Eunsol Choi, Hannaneh HajishirziNAACL • 2021 Multilingual question answering tasks typically assume that answers exist in the same language as the question. Yet in practice, many languages face both information scarcity—where languages have few reference articles—and information asymmetry—where…Probing Contextual Language Models for Common Ground with Visual Representations
Gabriel Ilharco, Rowan Zellers, Ali Farhadi, Hannaneh HajishirziNAACL • 2021 The success of large-scale contextual language models has attracted great interest in probing what is encoded in their representations. In this work, we consider a new question: to what extent contextual representations of concrete nouns are aligned with…DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts
Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A. Smith, Yejin ChoiACL • 2021 Despite recent advances in natural language generation, it remains challenging to control attributes of generated text. We propose DExperts: Decoding-time Experts, a decoding-time method for controlled text generation that combines a pretrained language model…DeLighT: Deep and Light-weight Transformer
Sachin Mehta, Marjan Ghazvininejad, Srini Iyer, Luke Zettlemoyer, Hannaneh HajishirziICLR • 2021 We introduce a very deep and light-weight transformer, DeLighT, that delivers similar or better performance than transformer-based models with significantly fewer parameters. DeLighT more efficiently allocates parameters both (1) within each Transformer block…Challenges in Algorithmic Debiasing for Toxic Language Detection
Xuhui Zhou, Maarten Sap, Swabha Swayamdipta, Noah A. Smith, Yejin ChoiEACL • 2021 Biased associations have been a challenge in the development of classifiers for detecting toxic language, hindering both fairness and accuracy. As potential solutions, we investigate recently introduced debiasing methods for text classification datasets and…Challenges in Automated Debiasing for Toxic Language Detection
Xuhui Zhou, Maarten Sap, Swabha Swayamdipta, Noah A. Smith, Yejin ChoiEACL • 2021 Biased associations have been a challenge in the development of classifiers for detecting toxic language, hindering both fairness and accuracy. As potential solutions, we investigate recently introduced debiasing methods for text classification datasets and…Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
Alon Jacovi, Ana Marasović, Tim Miller, Yoav GoldbergFAccT • 2021 Trust is a central component of the interaction between people and AI, in that 'incorrect' levels of trust may cause misuse, abuse or disuse of the technology. But what, precisely, is the nature of trust in AI? What are the prerequisites and goals of the…GENIE: A Leaderboard for Human-in-the-Loop Evaluation of Text Generation
Daniel Khashabi, Gabriel Stanovsky, Jonathan Bragg, Nicholas Lourie, Jungo Kasai, Yejin Choi, Noah A. Smith, Daniel S. WeldarXiv • 2021 Leaderboards have eased model development for many NLP datasets by standardizing their evaluation and delegating it to an independent external repository. Their adoption, however, is so far limited to tasks which can be reliably evaluated in an automatic…Green AI
Roy Schwartz, Jesse Dodge, Noah A. Smith, Oren EtzioniCACM • 2020 The computations required for deep learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018 [2]. These computations have a surprisingly large carbon footprint [38]. Ironically, deep learning was…A Simple Yet Strong Pipeline for HotpotQA
Dirk Groeneveld, Tushar Khot, Mausam, Ashish SabharwalEMNLP • 2020 State-of-the-art models for multi-hop question answering typically augment large-scale language models like BERT with additional, intuitively useful capabilities such as named entity recognition, graph-based reasoning, and question decomposition. However…