Flexible End-to-End Dialogue System for Knowledge Grounded Conversation

TL;DR

This paper introduces GenDS, a data-driven dialogue system capable of generating knowledge-grounded responses with multiple or no entities, handling out-of-vocabulary entities, and outperforming baselines on music and QA datasets.

Contribution

The paper presents a novel dynamic knowledge enquirer for dialogue generation that handles multiple entities and out-of-vocabulary cases without relying on entity representations.

Findings

GenDS outperforms baseline methods in BLEU, entity accuracy, and recall.

The system effectively generates responses with multiple or no entities.

GenDS performs well even on small datasets.

Abstract

In knowledge grounded conversation, domain knowledge plays an important role in a special domain such as Music. The response of knowledge grounded conversation might contain multiple answer entities or no entity at all. Although existing generative question answering (QA) systems can be applied to knowledge grounded conversation, they either have at most one entity in a response or cannot deal with out-of-vocabulary entities. We propose a fully data-driven generative dialogue system GenDS that is capable of generating responses based on input message and related knowledge base (KB). To generate arbitrary number of answer entities even when these entities never appear in the training set, we design a dynamic knowledge enquirer which selects different answer entities at different positions in a single response, according to different local context. It does not rely on the representations of entities, enabling our model deal with out-of-vocabulary entities. We collect a human-human conversation data (ConversMusic) with knowledge annotations. The proposed method is evaluated on CoversMusic and a public question answering dataset. Our proposed GenDS system outperforms baseline methods significantly in terms of the BLEU, entity accuracy, entity recall and human evaluation. Moreover,the experiments also demonstrate that GenDS works better even on small datasets.