QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization

08/31/2019
by   Yi-Ting Yeh, et al.
5

Standard accuracy metrics indicate that modern reading comprehension systems have achieved strong performance in many question answering datasets. However, the extent these systems truly understand language remains unknown, and existing systems are not good at distinguishing distractor sentences, which look related but do not actually answer the question. To address this problem, we propose QAInfomax as a regularizer in reading comprehension systems by maximizing mutual information among passages, a question, and its answer. QAInfomax helps regularize the model to not simply learn the superficial correlation for answering questions. The experiments show that our proposed QAInfomax achieves the state-of-the-art performance on the benchmark Adversarial-SQuAD dataset.

READ FULL TEXT
research
02/13/2022

PQuAD: A Persian Question Answering Dataset

We present Persian Question Answering Dataset (PQuAD), a crowdsourced re...
research
07/23/2017

Adversarial Examples for Evaluating Reading Comprehension Systems

Standard accuracy metrics indicate that reading comprehension systems ar...
research
11/29/2018

Multi-granularity hierarchical attention fusion networks for reading comprehension and question answering

This paper describes a novel hierarchical attention network for reading ...
research
03/08/2021

MCR-Net: A Multi-Step Co-Interactive Relation Network for Unanswerable Questions on Machine Reading Comprehension

Question answering systems usually use keyword searches to retrieve pote...
research
08/28/2018

Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Scientific Question Answering

Scientific Question Answering (SQA) is a challenging open-domain task wh...
research
01/15/2019

Incremental Reading for Question Answering

Any system which performs goal-directed continual learning must not only...
research
11/12/2017

Fast Reading Comprehension with ConvNets

State-of-the-art deep reading comprehension models are dominated by recu...

Please sign up or login with your details

Forgot password? Click here to reset