Knowledge-incorporating ESIM models for Response Selection in Retrieval-based Dialog Systems
This work addresses the problem of improving accuracy in goal-oriented dialog systems for applications like customer support, though it appears incremental as it builds on existing ESIM models.
The paper tackles response selection in retrieval-based dialog systems by extending ESIM models to incorporate external domain knowledge and leverage similar conversations, achieving performance improvements on the Ubuntu and Advising datasets in DSTC7.
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.