Red Dragon AI at TextGraphs 2019 Shared Task: Language Model Assisted Explanation Generation
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Abstract (arXiv)
The TextGraphs-13 Shared Task on Explanation Regeneration asked participants to develop methods to reconstruct gold explanations for elementary science questions. Red Dragon AI's entries used the language of the questions and explanation text directly, rather than a constructing a separate graph-like representation. Our leaderboard submission placed us 3rd in the competition, but we present here three methods of increasing sophistication, each of which scored successively higher on the test set after the competition close.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Nov 2019.
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Citing papers per year: 2021: 3.
3 papers.
- 2021Hybrid Autoregressive Inference for Scalable Multi-hop Explanation Regenerationcited by 17
- 2021Red Dragon AI at TextGraphs 2021 Shared Task: Multi-Hop Inference Explanation Regeneration by Matching Expert Ratingscited by 0
- 2021Ranking Facts for Explaining Answers to Elementary Science Questionscited by 0
- 2021Ranking Facts for Explaining Answers to Elementary Science Questions18 Oct 2021
- 2021Red Dragon AI at TextGraphs 2021 Shared Task: Multi-Hop Inference Explanation Regeneration by Matching Expert Ratings27 Jul 2021
- 2021Hybrid Autoregressive Inference for Scalable Multi-hop Explanation Regeneration25 Jul 2021