Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading (2020)

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 .

## introduction in conversational machine reading (cmr), machines can take the initiative to ask users questions that help to solve their problems, instead of jumping into a conclusion hurriedly @xcite . in this case, machines need to understand the knowledge base (kb) text, evaluate and keep track of the user scenario, ask clarification questions, and then make a final decision. this interactive behavior between users and machines has gained more attention recently because in practice users are unaware of the kb text, thus they cannot provide all the information needed in a single turn. no ## taking more leave than the entitlement if a worker has taken more leave than they're entitled to, their employer must not take money from their final pay unless it's been agreed beforehand in writing. the rules in this situation should be outlined in the employment contract, company handbook or intranet site. ## method as illustrated in figure 2 , our proposed method consists of the following four main modules. ## conclusions in this paper, we have proposed a new framework for conversational machine reading (cmr) that comprises a novel explicit memory tracker (emt) to track entailment states of the rule sentences explicitly within its memory module. the updated states are utilized for decision making and coarseto-fine follow-up question generation in a unified manner. emt achieved a new state-of-the-art result on the sharc cmr challenge. emt also gives interpretability by showing the entailment-oriented reasoning process as the conversation flows. while we conducted experiments on the sharc dataset, we believe the proposed methodology could be extended to other kinds of cmr tasks.