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Pushing it out of the Way: Interactive Visual Navigation
Kuo-Hao Zeng, Luca Weihs, A. Farhadi, R. MottaghiarXiv • 2021 We have observed significant progress in visual navigation for embodied agents. A common assumption in studying visual navigation is that the environments are static; this is a limiting assumption. Intelligent navigation may involve interacting with the…CODE: COMPILER-BASED NEURON-AWARE ENSEMBLE TRAINING
E. Trainiti, Thanapon Noraset, David Demeter, Doug Downey, Simone CampanoniProceedings of Machine Learning and Systems • 2021 Deep Neural Networks (DNNs) are redefining the state-of-the-art performance in a variety of tasks like speech recognition and image classification. These impressive results are often enabled by ensembling many DNNs together. Surprisingly, ensembling is often…Searching for Scientific Evidence in a Pandemic: An Overview of TREC-COVID
Kirk Roberts, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, Kyle Lo, I. Soboroff, E. Voorhees, Lucy Lu Wang, W. HersharXiv • 2021 We present an overview of the TREC-COVID Challenge, an information retrieval (IR) shared task to evaluate search on scientific literature related to COVID-19. The goals of TREC-COVID include the construction of a pandemic search test collection and the…Improving the Accessibility of Scientific Documents: Current State, User Needs, and a System Solution to Enhance Scientific PDF Accessibility for Blind and Low Vision Users
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. WeldarXiv • 2021 The majority of scientific papers are distributed in PDF, which pose challenges for accessibility, especially for blind and low vision (BLV) readers. We characterize the scope of this problem by assessing the accessibility of 11,397 PDFs published 2010--2019…ManipulaTHOR: A Framework for Visual Object Manipulation
Kiana Ehsani, Winson Han, Alvaro Herrasti, Eli VanderBilt, Luca Weihs, Eric Kolve, Aniruddha Kembhavi, R. MottaghiarXiv • 2021 The domain of Embodied AI has recently witnessed substantial progress, particularly in navigating agents within their environments. These early successes have laid the building blocks for the community to tackle tasks that require agents to actively interact…BERTese: Learning to Speak to BERT
Adi Haviv, Jonathan Berant, A. GlobersonEACL • 2021 Large pre-trained language models have been shown to encode large amounts of world and commonsense knowledge in their parameters, leading to substantial interest in methods for extracting that knowledge. In past work, knowledge was extracted by taking…Bootstrapping Relation Extractors using Syntactic Search by Examples
Matan Eyal, Asaf Amrami, Hillel Taub-Tabib, Yoav GoldbergEACL • 2021 The advent of neural-networks in NLP brought with it substantial improvements in supervised relation extraction. However, obtaining a sufficient quantity of training data remains a key challenge. In this work we propose a process for bootstrapping training…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…Discourse Understanding and Factual Consistency in Abstractive Summarization
Saadia Gabriel, Antoine Bosselut, Jeff Da, Ari Holtzman, Jan Buys, Kyle Lo, Asli Celikyilmaz, Yejin ChoiEACL • 2021 We introduce Cooperative Generator-Discriminator Networks (Co-opNet), a general framework for abstractive summarization with distinct modeling of the narrative flow in the output summary. Most current approaches to abstractive summarization, in contrast, are…