Systems and methods for explicit memory tracker with coarse-to-fine reasoning in conversational machine reading

Abstract

Embodiments described herein provide systems and methods for an Explicit Memory Tracker (EMT) that tracks each rule sentence to perform decision making and to generate follow-up clarifying questions. Specifically, the EMT first segments the regulation text into several rule sentences and allocates the segmented rule sentences into memory modules, and then feeds information regarding the user scenario and dialogue history into the EMT sequentially to update each memory module separately. At each dialogue turn, the EMT makes a decision among based on current memory status of the memory modules whether further clarification is needed to come up with an answer to a user question. The EMT determines that further clarification is needed by identifying an underspecified rule sentence span by modulating token-level span distributions with sentence-level selection scores. The EMT extracts the underspecified rule sentence span and rephrases the underspecified rule sentence span to generate a follow-up question.