Related papers: Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading

Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading

URL: http://arxiv.org/abs/2005.12484v2
Date: Tue, 23 Jun 2020 06:14:38 GMT
Title: Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading
Authors: Yifan Gao, Chien-Sheng Wu, Shafiq Joty, Caiming Xiong, Richard Socher, Irwin King, Michael R. Lyu, and Steven C.H. Hoi
Abstract summary: We present a new framework of conversational machine reading that comprises a novel Explicit Memory Tracker (EMT) Our framework generates clarification questions by adopting a coarse-to-fine reasoning strategy. EMT achieves new state-of-the-art results of 74.6% micro-averaged decision accuracy and 49.5 BLEU4.
Score: 177.50355465392047
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: The goal of conversational machine reading is to answer user questions given a knowledge base text which may require asking clarification questions. Existing approaches are limited in their decision making due to struggles in extracting question-related rules and reasoning about them. In this paper, we present a new framework of conversational machine reading that comprises a novel Explicit Memory Tracker (EMT) to track whether conditions listed in the rule text have already been satisfied to make a decision. Moreover, our framework generates clarification questions by adopting a coarse-to-fine reasoning strategy, utilizing sentence-level entailment scores to weight token-level distributions. On the ShARC benchmark (blind, held-out) testset, EMT achieves new state-of-the-art results of 74.6% micro-averaged decision accuracy and 49.5 BLEU4. We also show that EMT is more interpretable by visualizing the entailment-oriented reasoning process as the conversation flows. Code and models are released at https://github.com/Yifan-Gao/explicit_memory_tracker.