# Red Dragon AI at TextGraphs 2019 Shared Task: Language Model Assisted Explanation Generation

arXiv: https://arxiv.org/abs/1911.08976 · HTML: https://mlsanity.com/paper/1911.08976/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [Hybrid Autoregressive Inference for Scalable Multi-hop Explanation Regeneration](https://mlsanity.com/paper/2107.11879/) (2021)
- [Red Dragon AI at TextGraphs 2021 Shared Task: Multi-Hop Inference Explanation Regeneration by Matching Expert Ratings](https://mlsanity.com/paper/2107.13031/) (2021)
- [Ranking Facts for Explaining Answers to Elementary Science Questions](https://mlsanity.com/paper/2110.09036/) (2021)

## 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.