Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation (2020)

Abstract: Knowledge selection plays an important role in knowledge-grounded dialogue, which is a challenging task to generate more informative responses by leveraging external knowledge. Recently, latent variable models have been proposed to deal with the diversity of knowledge selection by using both prior and posterior distributions over knowledge and achieve promising performance. However, these models suffer from a huge gap between prior and posterior knowledge selection. Firstly, the prior selection module may not learn to select knowledge properly because of lacking the necessary posterior information. Secondly, latent variable models suffer from the exposure bias that dialogue generation is based on the knowledge selected from the posterior distribution at training but from the prior distribution at inference. Here, we deal with these issues on two aspects: (1) We enhance the prior selection module with the necessary posterior information obtained from the specially designed Posterior Information Prediction Module (PIPM); (2) We propose a Knowledge Distillation Based Training Strategy (KDBTS) to train the decoder with the knowledge selected from the prior distribution, removing the exposure bias of knowledge selection. Experimental results on two knowledge-grounded dialogue datasets show that both PIPM and KDBTS achieve performance improvement over the state-of-the-art latent variable model and their combination shows further improvement.

## introduction knowledge-grounded dialogue @xcite which leverages external knowledge to generate more informative responses, has become a popular research topic in recent years. many researchers have studied how to effectively leverage the given knowledge to enhance dialogue understanding and/or improve dialogue generation context i just got a husky puppy. ## approach in this section, we show how to bridge the gap between prior and posterior knowledge selection in knowledge-grounded dialogue. firstly, we design the posterior information prediction module (pipm) to enhance the prior selection module with the necessary posterior information. secondly, we design a knowledge distillation based training strategy (kdbts) to train the decoder with the knowledge selected from the prior distribution, removing the exposure bias of knowledge selection. ## related work we mainly focus on knowledge selection in open domain knowledge-grounded dialogue, and there are several work studying this issue @xcite @xcite . here, we point out the gap between prior and posterior knowledge selection and try to deal with this gap. the pipm has some relations with several work. deliberation decoder @xcite @xcite leverages two decoders for two-pass generation. we also use two decoders in pipm (y seq ), but the first decoder is used to generate posterior information for the knowledge selection rather than the second-pass generation. bow loss, proposed by @xcite , is adopted to supervise the posterior module @xcite . here, we have three different aspects: (1) we use the bow loss for the prior module rather than the posterior module; (2) we use posterior information in bow format to enhance the prior selection module; (3) we explore several bow variants. our kdbts is inspired by knowledge distillation @xcite . instead of using more complex structure as the teacher for model compression, we treat the posterior selection module with additional input information (e,g., the response) as the teacher, and deal with the exposure bias of knowledge selection. lite elbo @xcite is proposed to remove the exposure bias at latent space for a different task. however, lite elbo naturally does not leverage the posterior distribution as it sets the posterior module the same as the prior module. our kdbts is a two-stage training strategy that uses the posterior distribution as the teacher to guide the prior selection module and uses the knowledge selected from the prior distribution to train the decoder. ## conclusion in this paper, we firstly analyze the gap between prior and posterior knowledge selection for opendomain knowledge-grounded dialogue. then, we deal with it on two aspects: (1) we enhance the prior selection module with the posterior information obtained by the pipm and we explore several variants of posterior information. (2) we design the kdbts to train the decoder with knowledge selected from the prior distribution, removing the exposure bias of knowledge selection. experiments show that both pipm and kdbts improve the state-of-the-art latent variable model and their combination achieves further improvement. in the future, we would explore three aspects: (1) more efficient posterior information representation and corresponding prediction module, (2) the interpretability of knowledge selection and (3) knowledge selection without knowledge label.