Point2Token: A Multi-Tagging Answer Retrieval Framework for Question Answering

Abstract

Question answering plays a crucial role in the chatbot systems, in which it retrieves the answer from the given context and return the predicted span as a result to users. Previous work mostly modelled this task as a multi-classification problem. However, the models cannot gain a promising result due to the scarcity of the probability distribution over the whole given context. In this paper, we propose a novel approach to solve the problem mentioned above. We model the question answering task as a multiple binary classification problem and introduce PointerNet in our model decoder to predict whether it belongs to a start or end position in each token within context. The experimental results on a well-studied dataset show that our model outperforms the baseline models, which proves our model effectiveness.