Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading

Yifan Gao,Shafiq Joty,Chien-Sheng Wu,Irwin King,Caiming Xiong,Jingjing Li,Michael Lyu,Steven C.H Hoi

doi:10.18653/v1/2020.emnlp-main.191

Abstract

Document interpretation and dialog understanding are the two major challenges for conversational machine reading. In this work, we propose Discern, a discourse-aware entailment reasoning network to strengthen the connection and enhance the understanding of both document and dialog. Specifically, we split the document into clause-like elementary discourse units (EDU) using a pre-trained discourse segmentation model, and we train our model in a weakly-supervised manner to predict whether each EDU is entailed by the user feedback in a conversation. Based on the learned EDU and entailment representations, we either reply to the user our final decision yes/no/irrelevant of the initial question, or generate a follow-up question to inquiry more information. Our experiments on the ShARC benchmark (blind, held-out test set) show that Discern achieves state-of-the-art results of 78.3% macro-averaged accuracy on decision making and 64.0 BLEU1 on follow-up question generation. Code and models are released at https://github.com/Yifan-Gao/Discern.

Highlights

Conversational Machine Reading (CMR) is challenging because the rule text may not contain the literal answer, but provide a procedure to derive it through interactions (Saeidi et al, 2018)
It’s name comes from section 7(a) of the Small Business Act, which authorizes the agency to provide business loans to American small businesses
To see how DISCERN understands the logical structure of rules, we evaluate the decision making accuracy according to the logical types of rule texts

Summary

Introduction

Conversational Machine Reading (CMR) is challenging because the rule text may not contain the literal answer, but provide a procedure to derive it through interactions (Saeidi et al, 2018). In this case, the machine needs to read the rule text, interpret the user scenario, clarify the unknown user’s background by asking questions, and derive the final answer. Follow-up Q2: Are you able to get financing from other resources? Follow-up A2: No Final Answer: Yes. (You can apply the loan.)

Methods

Results

Conclusion