Learning to Express in Knowledge-Grounded Conversation (2022)

Abstract: Grounding dialogue generation by extra knowledge has shown great potentials towards building a system capable of replying with knowledgeable and engaging responses. Existing studies focus on how to synthesize a response with proper knowledge, yet neglect that the same knowledge could be expressed differently by speakers even under the same context. In this work, we mainly consider two aspects of knowledge expression, namely the structure of the response and style of the content in each part. We therefore introduce two sequential latent variables to represent the structure and the content style respectively. We propose a segmentation-based generation model and optimize the model by a variational approach to discover the underlying pattern of knowledge expression in a response. Evaluation results on two benchmarks indicate that our model can learn the structure style defined by a few examples and generate responses in desired content style.

## introduction building an open domain dialogue system has attracted increasing attention from the community of ai and nlp. despite the impressive progress, existing models are notorious for replying with generic and bland responses. to bridge the gap, researchers resort to ground dialogue generation by extra knowledge such as unstructured documents @xcite . by this means, the documents serve as content sources and make a dialogue system knowledgeable regarding various concepts in a discussion. however, existing studies focus on how to synthesize a response with proper knowledge @xcite @xcite , but pay little attention to the fact that the same knowledge could be expressed differently even under the same context. these models usually em- in general, the expression style of response is composed of two aspects: the structure of the response and the style of the content in each part. as the example shown in formally, our model follows an encoder-decoder architecture. the encoder is to obtain the contextual representation of conversational context and knowledge in a regular way. the decoder consists of three types of modules: (1) context module, for response only based on context without knowledge; (2) plain-knowledge module, for response referring knowledge but without particular style; and (3) stylized-knowledge module, for response referring knowledge and with a specific style. the context module is the only module not relying on knowledge, but simply paying attention to contextual information. compared with plain-knowledge module, stylized-knowledge module has unique adapters, which is their primary discrepancy. when decoding, the decoder first predicts the segmentation of the response and then makes a choice in three kinds of modules to generate a single segment. both the segmentation and the module selection are instructed under sequential latent variables. we train our model on the reddit corpus published by @xcite and evaluate our model on two benchmarks of knowledge-grounded conversation: wizard of wikipedia (wizard) @xcite and cmu document grounded conversation (cmu_dog) @xcite . evaluation results indicate that our model can significantly outperform state-of-the-art methods in the zeroresource setting (i.e., only trained on the reddit corpus). in addition, the performance of our model improves significantly on wizard and cmu_dog with the presence of only 10% training data and the segment distributions after fine-tuning are consistent with our prior knowledge about the two datasets, indicating that our model can learn the structure style with little cost. finally, our model outperforms previous state-of-the-art models on the accuracy of performing sentiment classification us-ing generated responses, which indicates that the model can be controlled to express knowledge with the desired content style. contributions in this work are three-fold: (1) exploration of the knowledge expression in knowledge-grounded conversation; (2) proposal of a variational segmentation-based generation model to discover the underlying expression style in a response; (3) empirical verification of the effectiveness of the proposed model on two benchmarks of knowledge-grounded conversation. ## related work on the vanilla encoder-decoder architecture @xcite , various extensions have been made to model the structure of dialogue contexts @xcite @xcite ; to improve diversity of responses @xcite @xcite ; to control attributes of responses @xcite @xcite ; and to bias responses to some specific personas @xcite . recently, grounding dialogue generation by extra knowledge has seemed promising to bridge the gap between conversation with existing systems and conversation with humans, and the knowledge could be obtained from knowledge graphs @xcite @xcite , retrieved from unstructured documents @xcite @xcite @xcite @xcite or visual background @xcite @xcite . in this work, we study document-grounded dialogue generation. rather than selecting knowledge relevant to dialogue context and directly exploiting pretrained language models to generate the response, we focus on expressing knowledge in this task. the idea of sequence modeling via segmentation @xcite has attracted widespread attention in several natural language processing tasks. in text segmentation task, @xcite propose a probabilistic model for sequence modeling via their segmentation and a "sleep-wake network" (swan) method. in machine translation, @xcite propose a neural phrase-based machine translation system that models phrase structures in the target language using swan. in data-to-text generation, @xcite develop a neural template-like genera-tion model with a hsmm decoder, which is learned tractably by backpropagating through a dynamic program; to tackle the problem of weak markov assumption for the segment transition probability, @xcite propose to explicitly segment target text into fragments and align them with their data correspondences, and jointly learn the segmentation and correspondence via dynamic programming. ## approach we start by providing the problem formalization and overview of the proposed model in sec.3.1. then in sec.3.2 we describe the design for each components. lastly, we elaborate how to optimize the components with variational inference and weak supervision in sec.3.3. ## conclusions we explore knowledge expression in knowledgegrounded conversation and break down the expression style of a response into the structure of the response (structure style) and the style of the content in each part (content style). we propose a variational segmentation-based generation model to discover the underlying expression style in response. specifically, we introduce two latent variables to model these two aspects of expression style respectively and induce an evidence lower bound of the likelihood. evaluation results on two benchmarks of the task indicate that our model can learn the structure style with little cost and generate responses in desired content style without any human-annotated data.