Abstract
Explainable question answering for science questions is a challenging task that requires multi-hop inference over a large set of fact sentences. To counter the limitations of methods that view each query-document pair in isolation, we propose the LSTM-Interleaved Transformer which incorporates cross-document interactions for improved multi-hop ranking. The LIT architecture can leverage prior ranking positions in the re-ranking setting. Our model is competitive on the current leaderboard for the TextGraphs 2020 shared task, achieving a test-set MAP of 0.5607, and would have gained third place had we submitted before the competition deadline. Our code implementation is made available at [https://github.com/mdda/worldtree_corpus/tree/textgraphs_2020](https://github.com/mdda/worldtree_corpus/tree/textgraphs_2020).- Anthology ID:
- 2020.textgraphs-1.14
- Volume:
- Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Venue:
- TextGraphs
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 115–120
- Language:
- URL:
- https://aclanthology.org/2020.textgraphs-1.14
- DOI:
- 10.18653/v1/2020.textgraphs-1.14
- Cite (ACL):
- Yew Ken Chia, Sam Witteveen, and Martin Andrews. 2020. Red Dragon AI at TextGraphs 2020 Shared Task : LIT : LSTM-Interleaved Transformer for Multi-Hop Explanation Ranking. In Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs), pages 115–120, Barcelona, Spain (Online). Association for Computational Linguistics.
- Cite (Informal):
- Red Dragon AI at TextGraphs 2020 Shared Task : LIT : LSTM-Interleaved Transformer for Multi-Hop Explanation Ranking (Chia et al., TextGraphs 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.textgraphs-1.14.pdf
- Code
- mdda/worldtree_corpus
- Data
- Worldtree