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TIMEDIAL: Temporal Commonsense Reasoning in Dialog
Lianhui Qin, Aditya Gupta, Shyam Upadhyay, Luheng He, Yejin Choi, Manaal FaruquiACL • 2021 Everyday conversations require understanding everyday events, which in turn, requires understanding temporal commonsense concepts interwoven with those events. Despite recent progress with massive pre-trained language models (LMs) such as T5 and GPT-3, their…A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers
Pradeep Dasigi, Kyle Lo, Iz Beltagy, Arman Cohan, Noah A. Smith, Matt GardnerNAACL • 2021 Readers of academic research papers often read with the goal of answering specific questions. Question Answering systems that can answer those questions can make consumption of the content much more efficient. However, building such tools requires data that…Choose Your Own Adventure: Paired Suggestions in Collaborative Writing for Evaluating Story Generation Models
Elizabeth Clark, Noah A. SmithNAACL • 2021 Story generation is an open-ended and subjective task, which poses a challenge for evaluating story generation models. We present Choose Your Own Adventure, a collaborative writing setup for pairwise model evaluation. Two models generate suggestions to people…Extracting a Knowledge Base of Mechanisms from COVID-19 Papers
Aida Amini, T. Hope, David Wadden, Madeleine van Zuylen, E. Horvitz, Roy Schwartz, Hannaneh HajishirziNAACL • 2021 The urgency of mitigating COVID-19 has spawned a large and diverse body of scientific literature that is challenging for researchers to navigate. This explosion of information has stimulated interest in automated tools to help identify useful knowledge. We…"I'm Not Mad": Commonsense Implications of Negation and Contradiction
Liwei Jiang, Antoine Bosselut, Chandra Bhagavatula, Yejin ChoiNAACL • 2021 Natural language inference requires reasoning about contradictions, negations, and their commonsense implications. Given a simple premise (e.g., “I’m mad at you”), humans can reason about the varying shades of contradictory statements ranging from…SmBoP: Semi-autoregressive Bottom-up Semantic Parsing
Ohad Rubin and Jonathan BerantNAACL • 2021 The de-facto standard decoding method for semantic parsing in recent years has been to autoregressively decode the abstract syntax tree of the target program using a top-down depth-first traversal. In this work, we propose an alternative approach: a Semi…Temporal Reasoning on Implicit Events from Distant Supervision
Ben Zhou, Kyle Richardson, Qiang Ning, Tushar Khot, Ashish Sabharwal, D. RothNAACL • 2021 Existing works on temporal reasoning among events described in text focus on modeling relationships between explicitly mentioned events and do not handle event end time effectively. However, human readers can infer from natural language text many implicit…Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models
Tushar Khot, Daniel Khashabi, Kyle Richardson, Peter Clark, Ashish SabharwalNAACL • 2021 A common approach to solve complex tasks is by breaking them down into simple sub-problems that can then be solved by simpler modules. However, these approaches often need to be designed and trained specifically for each complex task. We propose a general…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…