Knowledge-incorporating ESIM models for Response Selection in Retrieval-based Dialog Systems

TL;DR

This paper introduces knowledge-enhanced ESIM models for response selection in retrieval-based dialog systems, demonstrating improved accuracy by integrating external domain knowledge and similar conversation information.

Contribution

It proposes two novel ESIM-based models, K-ESIM and T-ESIM, that incorporate external knowledge and similar dialog data, advancing response prediction in goal-oriented dialog systems.

Findings

Incorporating external knowledge improves response selection accuracy.

Leveraging similar conversation data enhances model performance.

Models outperform baseline ESIM on DSTC7 datasets.

Abstract

Goal-oriented dialog systems, which can be trained end-to-end without manually encoding domain-specific features, show tremendous promise in the customer support use-case e.g. flight booking, hotel reservation, technical support, student advising etc. These dialog systems must learn to interact with external domain knowledge to achieve the desired goal e.g. recommending courses to a student, booking a table at a restaurant etc. This paper presents extended Enhanced Sequential Inference Model (ESIM) models: a) K-ESIM (Knowledge-ESIM), which incorporates the external domain knowledge and b) T-ESIM (Targeted-ESIM), which leverages information from similar conversations to improve the prediction accuracy. Our proposed models and the baseline ESIM model are evaluated on the Ubuntu and Advising datasets in the Sentence Selection track of the latest Dialog System Technology Challenge (DSTC7), where the goal is to find the correct next utterance, given a partial conversation, from a set of candidates. Our preliminary results suggest that incorporating external knowledge sources and leveraging information from similar dialogs leads to performance improvements for predicting the next utterance.