SciTaiL: A Textual Entailment Dataset from Science Question Answering

@inproceedings{Khot2018SciTaiLAT,
  title={SciTaiL: A Textual Entailment Dataset from Science Question Answering},
  author={Tushar Khot and Ashish Sabharwal and Peter Clark},
  booktitle={AAAI},
  year={2018}
}
We present a new dataset and model for textual entailment, derived from treating multiple-choice question-answering as an entailment problem. SCITAIL is the first entailment set that is created solely from natural sentences that already exist independently “in the wild” rather than sentences authored specifically for the entailment task. Different from existing entailment datasets, we create hypotheses from science questions and the corresponding answer candidates, and premises from relevant… CONTINUE READING

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Key Quantitative Results

  • As a step forward, we demonstrate that one can improve accuracy on SCITAIL by 5% using a new neural model that exploits linguistic structure.
  • On the other hand, our structurebased approach is able to achieve about 5% gain over the best baseline system on this task.

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