Papers

Viewing 1-10 of 47 papers
  • Visual Commonsense Graphs: Reasoning about the Dynamic Context of a Still Image

    Jae Sung Park, Chandra Bhagavatula, Roozbeh Mottaghi, Ali Farhadi, Yejin Choi ECCV2020Even from a single frame of a still image, people can reason about the dynamic story of the image before, after, and beyond the frame. For example, given an image of a man struggling to stay afloat in water, we can reason that the man fell into the water sometime in the past, the intent of that man… more
  • Adversarial Filters of Dataset Biases

    Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula, Rowan Zellers, Matthew E. Peters, Ashish Sabharwal, Yejin Choi ICML2020Large neural models have demonstrated humanlevel performance on language and vision benchmarks such as ImageNet and Stanford Natural Language Inference (SNLI). Yet, their performance degrades considerably when tested on adversarial or out-of-distribution samples. This raises the question of whether… more
  • 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
    Best Paper Award Honorable Mention
    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 task. We present a study across four… more
  • Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models

    Maarten Sap, Eric Horvitz, Yejin Choi, Noah A. Smith, James W. Pennebaker ACL2020We investigate the use of NLP as a measure of the cognitive processes involved in storytelling, contrasting imagination and recollection of events. To facilitate this, we collect and release HIPPOCORPUS, a dataset of 7,000 stories about imagined and recalled events. We introduce a measure of… more
  • Social Bias Frames: Reasoning about Social and Power Implications of Language

    Maarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, Yejin ChoiACL2020Language 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 seemingly innocuous statement "we… more
  • The Right Tool for the Job: Matching Model and Instance Complexities

    Roy Schwartz, Gabi Stanovsky, Swabha Swayamdipta, Jesse Dodge, Noah A. SmithACL2020As NLP models become larger, executing a trained model requires significant computational resources incurring monetary and environmental costs. To better respect a given inference budget, we propose a modification to contextual representation fine-tuning which, during inference, allows for an early… more
  • Analysis of the Penn Korean Universal Dependency Treebank (PKT-UD): Manual Revision to Build Robust Parsing Model in Korean

    Tae Hwan Oh, Ji Yoon Han, Hyonsu Choe, Seok-Won Park, Han He, Jinho D. Choi, Na-Rae Han, Jena D. Hwang, Hansaem Kim arXiv2020In this paper, we first open on important issues regarding the Penn Korean Universal Treebank (PKT-UD) and address these issues by revising the entire corpus manually with the aim of producing cleaner UD annotations that are more faithful to Korean grammar. For compatibility to the rest of UD… more
  • Abductive Commonsense Reasoning

    Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Hannah Rashkin, Doug Downey, Scott Wen-tau Yih, Yejin ChoiICLR2020Abductive reasoning is inference to the most plausible explanation. For example, if Jenny finds her house in a mess when she returns from work, and remembers that she left a window open, she can hypothesize that a thief broke into her house and caused the mess, as the most plausible explanation… more
  • G-DAUG: Generative Data Augmentation for Commonsense Reasoning

    Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, Doug Downey arXiv2020Recent advances in commonsense reasoning depend on large-scale human-annotated training data to achieve peak performance. However, manual curation of training examples is expensive and has been shown to introduce annotation artifacts that neural models can readily exploit and overfit on. We… more
  • Evaluating Machines by their Real-World Language Use

    Rowan Zellers, Ari Holtzman, Elizabeth Anne Clark, Lianhui Qin, Ali Farhadi, Yejin ChoiarXiv2020There is a fundamental gap between how humans understand and use language – in openended, real-world situations – and today’s NLP benchmarks for language understanding. To narrow this gap, we propose to evaluate machines by their success at real-world language use – which greatly expands the scope… more