Answer sentence ranking and answer extraction are two key challenges in question answering that have traditionally been treated in isolation, i.e., as independent tasks. In this article, we (1) explain how both tasks are related at their core by a common quantity, and (2) propose a simple and intuitive joint probabilistic model that addresses both via joint computation but task-specific application of that quantity. In our experiments with two TREC datasets, our joint model substantially outperforms state-of-the-art systems in both tasks.
Recent work REF ) models answer sentence extraction and answer extraction jointly.
A Joint Model for Answer Sentence Ranking and Answer Extraction
Transactions of the Association for Computational Linguistics
Transactions of the Association for Computational Linguistics
Computer Science