Seeing things from a different angle: Discovering diverse perspectives about claims

Sihao Chen, Daniel Khashabi, Wenpeng Yin, Chris Callison-Burch, Dan Roth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

63 Scopus citations

Abstract

One key consequence of the information revolution is a significant increase and a contamination of our information supply. The practice of fact-checking won't suffice to eliminate the biases in text data we observe, as the degree of factuality alone does not determine whether biases exist in the spectrum of opinions visible to us. To better understand controversial issues, one needs to view them from a diverse yet comprehensive set of perspectives. For example, there are many ways to respond to a claim such as “animals should have lawful rights”, and these responses form a spectrum of perspectives, each with a stance relative to this claim and, ideally, with evidence supporting it. Inherently, this is a natural language understanding task, and we propose to address it as such. Specifically, we propose the task of substantiated perspective discovery where, given a claim, a system is expected to discover a diverse set of well-corroborated perspectives that take a stance with respect to the claim. Each perspective should be substantiated by evidence paragraphs which summarize pertinent results and facts. We construct PERSPECTRUM, a dataset of claims, perspectives and evidence, making use of online debate websites to create the initial data collection, and augmenting it using search engines in order to expand and diversify our dataset. We use crowdsourcing to filter out noise and ensure high-quality data. Our dataset contains 1k claims, accompanied by pools of 10k and 8k perspective sentences and evidence paragraphs, respectively. We provide a thorough analysis of the dataset to highlight key underlying language understanding challenges, and show that human baselines across multiple subtasks far outperform machine baselines built upon state-of-the-art NLP techniques. This poses a challenge and an opportunity for the NLP community to address.

Original languageEnglish (US)
Title of host publicationLong and Short Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages542-557
Number of pages16
ISBN (Electronic)9781950737130
StatePublished - 2019
Event2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019 - Minneapolis, United States
Duration: Jun 2 2019Jun 7 2019

Publication series

NameNAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
Volume1

Conference

Conference2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019
Country/TerritoryUnited States
CityMinneapolis
Period6/2/196/7/19

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Computer Science Applications
  • Linguistics and Language

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