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Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading
[article]
2020
arXiv
pre-print
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
arXiv:2005.12484v2
fatcat:d32ijotxlzg23dw5loq5fnf3su