Context and knowledge aware conversational model and system combination for grounded response generation. (July 2020)

• Record Type: Journal Article
• Authors: Tanaka, Ryota; Ozeki, Akihide; Kato, Shugo; Lee, Akinobu
• Is Part Of: Computer speech & language. Volume 62(2020)
• Publication Date: 2020-07
• Subjects: DSTC -- Dialogue system -- Conversational AI -- Sentence generation -- Grounding knowledge; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2020.101070
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: We propose MHRED grounded on both multi-turn dialogue context and facts. System combination that re-ranks the generated responses from generation- and retrieval-based dialogue systems is applied. MHRED outperforms strong baselines on especially diversity scores and human evaluations. Combining multiple hypotheses significantly improves on all evaluations. Abstract: End-to-end neural-based dialogue systems can potentially generate tailored and coherent responses for user inputs. However, most of existing systems produce universal and non-informative responses, and they have not gone beyond chitchat yet. To tackle these problems, 7th Dialog System Technology Challenges (DSTC7-Track2) was developed to focus on building a dialogue system that produces informational responses that are grounded on external knowledge. In this study, we propose a Memory-augmented Hierarchical Recurrent Encoder-Decoder, called MHRED, that grounded on both multi-turn dialogue context and external knowledge. Furthermore, we apply a combination of multiple dialogue systems. Our final system is an ensemble that combines three modules: a generation-based module, a retrieval-based module, and a reranking module. First, responses are generated by MHRED, and retrieved from a pre-defined database focusing on informativeness. Next, the reranking module sorts these candidates using several hand-crafted features, and finally it selects a response with the highest score. Therefore, this system can return diverse and meaningful responses from various perspectives. Experimental results show that our proposed MHRED outperforms strong baseline models and combining multiple dialogue systems significantly improves the automatic evaluation and human evaluations.