Cluster-based beam search for pointer-generator chatbot grounded by knowledge. (November 2020) - Tam, Yik-Cheung
Highlights: Proposed an end-to-end model to incorporate conversation history and facts with copy mechanism. Proposed a new beam search strategy to improve response diversity driven by K-means clustering. Proposed a language modeling approach to filter out safe responses. Evaluated on DSTC7 and achieved the first place. Post-evaluated system with word-piece tokenization. Abstract: We present an end-to-end approach for knowledge-grounded response generation in Dialog System Technology Challenges 7 (DSTC7). Our system is trained by a pointer generator model, so that an output token in a response can either be generated or copied from conversation history or facts according to a trainable action probability distribution. Furthermore, to minimize generating meaningless responses, we propose using K-means to dynamically cluster and prune semantically similar partial hypotheses at each decoding step under a fixed beam budget. Moreover, we employ a language model to filter meaningless responses. Official evaluation results show that our proposed system achieved the first place in all primary automatic evaluation metrics and the overall human evaluation score.