Approximation of Response Knowledge Retrieval in Knowledge-grounded Dialogue Generation

By Wen Zheng, Natasa Milic-Frayling, Ke Zhou. 2020

8 citations recorded by the source corpus.

This paper is concerned with improving dialogue generation models through injection of knowledge, e.g., content relevant to the post that can increase the quality of responses. Past research extends the training of the generative models by incorporating statistical properties of posts, responses and related knowledge, without explicitly assessing the knowledge quality. In our work, we demonstrate the importance of knowledge relevance and adopt a two-phase approach. We first apply a novel method, Transformer & Post based Posterior Approximation (TPPA) to select knowledge, and then use the Transformer with Expanded Decoder (TED) model to generate responses from both the post and the knowledge. TPPA method processes posts, post related knowledge, and response related knowledge at both word and sentence level. Our experiments with the TED generative model demonstrate the effectiveness of TPPA as it outperforms a set of strong baseline models. Our TPPA method is extendable and supports further optimization of knowledge retrieval and injection.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Natasa Milic-Frayling
2. Ke Zhou
3. Wen Zheng

Citations in the corpus

• Knowledge-Grounded Dialogue Generation with Term-level De-noising (2021, background): “…role the ‘post’ and the second role the ‘response’ and we aim to generate the response for the given post. noise, i.e., for inclusion of non-relevant words, and previous studies (Galley et al. (2019), Zheng et al. (2020)) have shown that adding noise can decrease the response generation quality.”