Learning to Combine Answer Boundary Detection and Answer Re-ranking for Phrase-Indexed Question Answering  

作　　者：WEN Liang SHI Haibo ZHANG Xiaodong SUN Xin WEI Xiaochi WANG Junfeng CHENG Zhicong YIN Dawei WANG Xiaolin LUO Yingwei WANG Houfeng 

机构地区：[1]Department of Computer Science and Technology,Peking University,Beijing 100871,China [2]Peng Cheng Laboratory,Shenzhen 518052,China [3]Baidu Inc.,Beijing 100193,China

出　　处：《Chinese Journal of Electronics》2022年第5期938-948,共11页电子学报（英文版）

基　　金：supported by National Natural Science Foundation of China(62036001,62032001);PKU-Baidu Fund(2020BD021).

摘　　要：Phrase-indexed question answering(PIQA)seeks to improve the inference speed of question answering(QA)models by enforcing complete independence of the document encoder from the question encoder,and it shows that the constrained model can achieve significant efficiency at the cost of its accuracy.In this paper,we aim to build a model under the PIQA constraint while reducing its accuracy gap with the unconstrained QA models.We propose a novel framework-AnsDR,which consists of an answer boundary detector(AnsD)and an answer candidate ranker(AnsR).More specifically,AnsD is a QA model under the PIQA architecture and it is designed to identify the rough answer boundaries;and AnsR is a lightweight ranking model to finely rerank the potential candidates without losing the efficiency.We perform the extensive experiments on public datasets.The experimental results show that the proposed method achieves the state of the art on the PIQA task.

关 键 词：Real-time question answering Answer boundary detection Answer re-ranking Phrase-indexed question answering Answer span aware loss 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]