ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking

Pratyay Banerjee


Abstract
In this work we describe the system from Natural Language Processing group at Arizona State University for the TextGraphs 2019 Shared Task. The task focuses on Explanation Regeneration, an intermediate step towards general multi-hop inference on large graphs. Our approach consists of modeling the explanation regeneration task as a learning to rank problem, for which we use state-of-the-art language models and explore dataset preparation techniques. We utilize an iterative reranking based approach to further improve the rankings. Our system secured 2nd rank in the task with a mean average precision (MAP) of 41.3% on the test set.
Anthology ID:
D19-5310
Volume:
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)
Month:
November
Year:
2019
Address:
Hong Kong
Venues:
EMNLP | TextGraphs | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
78–84
Language:
URL:
https://aclanthology.org/D19-5310
DOI:
10.18653/v1/D19-5310
Bibkey:
Cite (ACL):
Pratyay Banerjee. 2019. ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking. In Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13), pages 78–84, Hong Kong. Association for Computational Linguistics.
Cite (Informal):
ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking (Banerjee, EMNLP 2019)
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PDF:
https://aclanthology.org/D19-5310.pdf
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