An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective
Jie Wen Zhao, Yu Su, Ziyu Guan, Huan Sun · 2017
Given a question and a set of answer candidates, answer triggering determines whether the candidate set contains any correct answers.If yes, it then outputs a correct one.In contrast to existing pipeline methods which first consider individual candidate answers separately and then make a prediction based on a threshold, we propose an end-to-end deep neural network framework, which is trained by a novel group-level objective function that directly optimizes the answer triggering performance.Our objective function penalizes three potential types of error and allows training the framework in an end-to-end manner.Experimental results on the WIKIQA benchmark show that our framework outperforms the state of the arts by a 6.6% absolute gain under F 1 measure 1 .