Corpus ID: 222178328

Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering

@article{Jhamtani2020LearningTE,
  title={Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering},
  author={Harsh Jhamtani and P. Clark},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.03274}
}
  • Harsh Jhamtani, P. Clark
  • Published 2020
  • Computer Science
  • ArXiv
  • Despite the rapid progress in multihop question-answering (QA), models still have trouble explaining why an answer is correct, with limited explanation training data available to learn from. To address this, we introduce three explanation datasets in which explanations formed from corpus facts are annotated. Our first dataset, eQASC, contains over 98K explanation annotations for the multihop question answering dataset QASC, and is the first that annotates multiple candidate explanations for… CONTINUE READING

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 27 REFERENCES
    HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
    • 276
    • Highly Influential
    • PDF
    BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
    • 10,032
    • PDF
    Constructing Datasets for Multi-hop Reading Comprehension Across Documents
    • 190
    • PDF
    Bidirectional Attention Flow for Machine Comprehension
    • 1,143
    • PDF
    CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
    • 110
    • PDF
    QASC: A Dataset for Question Answering via Sentence Composition
    • 22
    • Highly Influential
    • PDF
    SciTaiL: A Textual Entailment Dataset from Science Question Answering
    • 148
    Explain Yourself! Leveraging Language Models for Commonsense Reasoning
    • 60
    • Highly Influential
    • PDF