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 Ashutosh 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

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.
SHOWING 1-10 OF 42 CITATIONS, ESTIMATED 61% COVERAGE

68 Citations

020406020182019
Citations per Year
Semantic Scholar estimates that this publication has 68 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
SHOWING 1-10 OF 32 REFERENCES

Similar Papers

Loading similar papers…