ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking
This work addresses multi-hop inference for natural language processing researchers, but it is incremental as it applies existing methods to a specific shared task.
The paper tackled the Explanation Regeneration task in the TextGraphs 2019 Shared Task by modeling it as a learning-to-rank problem using language models and iterative re-ranking, achieving second place with a mean average precision of 41.3% on the test set.
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 \textit{learning to rank} problem, for which we use state-of-the-art language models and explore dataset preparation techniques. We utilize an iterative re-ranking 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.