Viewing 1-10 of 112 papers
  • Transformers as Soft Reasoners over Language

    Peter Clark, Oyvind Tafjord, Kyle RichardsonIJCAI2020AI has long pursued the goal of having systems reason over explicitly provided knowledge, but building suitable representations has proved challenging. Here we explore whether transformers can similarly learn to reason (or emulate reasoning), but using rules expressed in language, thus bypassing a… more
  • Multi-class Hierarchical Question Classification for Multiple Choice Science Exams

    Dongfang Xu, Peter Jansen, Jaycie Martin, Zhengnan Xie, Vikas Yadav, Harish Tayyar Madabushi, Oyvind Tafjord, Peter ClarkIJCAI2020Prior work has demonstrated that question classification (QC), recognizing the problem domain of a question, can help answer it more accurately. However, developing strong QC algorithms has been hindered by the limited size and complexity of annotated data available. To address this, we present the… more
  • TransOMCS: From Linguistic Graphs to Commonsense Knowledge

    Hongming Zhang, Daniel Khashabi, Yangqiu Song, Dan RothIJCAI2020Commonsense knowledge acquisition is a key problem for artificial intelligence. Conventional methods of acquiring commonsense knowledge generally require laborious and costly human annotations, which are not feasible on a large scale. In this paper, we explore a practical way of mining commonsense… more
  • Not All Claims are Created Equal: Choosing the Right Approach to Assess Your Hypotheses

    Erfan Sadeqi Azer, Daniel Khashabi, Ashish Sabharwal, Dan RothACL2020Empirical research in Natural Language Processing (NLP) has adopted a narrow set of principles for assessing hypotheses, relying mainly on p-value computation, which suffers from several known issues. While alternative proposals have been well-debated and adopted in other fields, they remain rarely… more
  • Temporal Common Sense Acquisition with Minimal Supervision

    Ben Zhou, Qiang Ning, Daniel Khashabi, Dan RothACL 2020Temporal common sense (e.g., duration and frequency of events) is crucial for understanding natural language. However, its acquisition is challenging, partly because such information is often not expressed explicitly in text, and human annotation on such concepts is costly. This work proposes a… more
  • What-if I ask you to explain: Explaining the effects of perturbations in procedural text

    Dheeraj Rajagopal, Niket Tandon, Peter Clarke, Bhavana Dalvi, Eduard H. HovyarXiv2020We address the task of explaining the effects of perturbations in procedural text, an important test of process comprehension. Consider a passage describing a rabbit's life-cycle: humans can easily explain the effect on the rabbit population if a female rabbit becomes ill -- i.e., the female rabbit… more
  • GenericsKB: A Knowledge Base of Generic Statements

    Sumithra Bhakthavatsalam, Chloe Anastasiades, Peter ClarkarXiv2020We present a new resource for the NLP community, namely a large (3.5M+ sentence) knowledge base of *generic statements*, e.g., "Trees remove carbon dioxide from the atmosphere", collected from multiple corpora. This is the first large resource to contain *naturally occurring* generic sentences, as… more
  • UnifiedQA: Crossing Format Boundaries With a Single QA System

    Daniel Khashabi, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, Hannaneh Hajishirzi arXiv2020Question answering (QA) tasks have been posed using a variety of formats, such as extractive span selection, multiple choice, etc. This has led to format-specialized models, and even to an implicit division in the QA community. We argue that such boundaries are artificial and perhaps unnecessary… more
  • Modular Representation Underlies Systematic Generalization in Neural Natural Language Inference Models

    Atticus Geiger, Kyle Richardson, Christopher PottsarXiv2020In adversarial (challenge) testing, we pose hard generalization tasks in order to gain insights into the solutions found by our models. What properties must a system have in order to succeed at these hard tasks? In this paper, we argue that an essential factor is the ability to form modular… more
  • A Simple Yet Strong Pipeline for HotpotQA

    Dirk Groeneveld, Tushar Khot, Mausam, Ashish SabharwalarXiv2020State-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, does their strong performance on popular… more