A Joint Model for Answer Sentence Ranking and Answer Extraction
Published
2016-04-18
Md Arafat Sultan
,
Vittorio Castelli
,
Radu Florian
Md Arafat Sultan
Institute of Cognitive Science and Department of Computer Science, University of Colorado Boulder
Vittorio Castelli
IBM Research
Radu Florian
IBM Research
Abstract
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.
PDF (presented at NAACL 2016)