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ACL Findings2021

TellMeWhy: A Dataset for Answering Why-Questions in Narratives

Yash Kumar Lal, Nathanael Chambers, Raymond J. Mooney, Niranjan Balasubramanian

Abstract

Answering questions about why characters perform certain actions is central to understanding and reasoning about narratives. Despite recent progress in QA, it is not clear if existing models have the ability to answer "why" questions that may require commonsense knowledge external to the input narrative. In this work, we introduce TellMeWhy, a new crowd-sourced dataset that consists of more than 30k questions and free-form answers concerning why characters in short narratives perform the actions described. For a third of this dataset, the answers are not present within the narrative. Given the limitations of automated evaluation for this task, we also present a systematized human evaluation interface for this dataset. Our evaluation of state-of-the-art models show that they are far below human performance on answering such questions. They are especially worse on questions whose answers are external to the narrative, thus providing a challenge for future QA and narrative understanding research.

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Cited by top-tier papers12

  1. Fantastic Questions and Where to Find Them: FairytaleQA - An Authentic Dataset for Narrative ComprehensionYing Xu, Dakuo Wang, Mo Yu, Daniel Ritchie et al.ACL 2022 · 131 citations
  2. CLadder: A Benchmark to Assess Causal Reasoning Capabilities of Language ModelsZhijing Jin, Yuen Chen, Felix Leeb, Luigi Gresele et al.NeurIPS 2023 · 74 citations
  3. LogiGAN: Learning Logical Reasoning via Adversarial Pre-trainingXinyu Pi, Wanjun Zhong, Yan Gao, Nan Duan et al.NeurIPS 2022 · 19 citations
  4. COLD: Causal reasOning in cLosed Daily activitiesAbhinav Joshi, Areeb Ahmad, Ashutosh ModiNeurIPS 2024 · 11 citations
  5. WikiWhy: Answering and Explaining Cause-and-Effect QuestionsMatthew Ho, Aditya Sharma, Justin Chang, Michael Saxon et al.ICLR 2023 · 8 citations

Builds on7

  1. BERTScore: Evaluating Text Generation with BERTTianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger et al.ICLR 2020 · 8,443 citations
  2. Abductive Commonsense ReasoningChandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi et al.ICLR 2020 · 521 citations
  3. UnifiedQA: Crossing Format Boundaries With a Single QA SystemDaniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal et al.EMNLP Findings 2020 · 514 citations
  4. Getting Closer to AI Complete Question Answering: A Set of Prerequisite Real TasksAnna Rogers, Olga Kovaleva, Matthew Downey, Anna RumshiskyAAAI 2020 · 141 citations
  5. BLEURT: Learning Robust Metrics for Text GenerationThibault Sellam, Dipanjan Das, Ankur P. ParikhACL 2020 · 40 citations

Related papers

  1. Using Commonsense Knowledge to Answer Why-QuestionsYash Kumar Lal, Niket Tandon, Tanvi Aggarwal, Horace Liu et al.EMNLP 2022 · 7 citations
  2. PragmatiCQA: A Dataset for Pragmatic Question Answering in ConversationsPeng Qi, Nina Du, Christopher D. Manning, Jing HuangACL Findings 2023 · 2 citations
  3. ProtoQA: A Question Answering Dataset for Prototypical Common-Sense ReasoningMichael Boratko, Xiang Li, Tim O'Gorman, Rajarshi Das et al.EMNLP 2020 · 6 citations
  4. What Do You Mean 'Why?': Resolving Sluices in ConversationsVictor Petrén Bach Hansen, Anders SøgaardAAAI 2020 · 11 citations
  5. ePiC: Employing Proverbs in Context as a Benchmark for Abstract Language UnderstandingSayan Ghosh, Shashank SrivastavaACL 2022
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