Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation (2022)

Abstract: Knowledge-grounded dialogue systems are challenging to build due to the lack of training data and heterogeneous knowledge sources. Existing systems perform poorly on unseen topics due to limited topics covered in the training data. In addition, it is challenging to generalize to the domains that require different types of knowledge sources. To address the above challenges, we present PLUG, a language model that homogenizes different knowledge sources to a unified knowledge representation for knowledge-grounded dialogue generation tasks. We first retrieve relevant information from heterogeneous knowledge sources (e.g., wiki, dictionary, or knowledge graph); Then the retrieved knowledge is transformed into text and concatenated with dialogue history to feed into the language model for generating responses. PLUG is pre-trained on a large-scale knowledge-grounded dialogue corpus. The empirical evaluation on two benchmarks shows that PLUG generalizes well across different knowledge-grounded dialogue tasks. It achieves comparable performance with state-of-the-art methods in the fully-supervised setting and significantly outperforms other approaches in zero-shot and few-shot settings.

## introduction recent work has shown that conversational models can be trained in an end-to-end fashion @xcite @xcite . though such models can generate coherent and natural responses consistent with conversation history, there is still a clear gap between conversational ai agents and humans. the primary reason is that existing dialogue systems lack knowledge of the subject and thus cannot deep dive into specific topics with humans. in order to better incorporate knowledge into dialogue, knowledge-grounded dialogue systems have become increasingly popular. knowledge-grounded dialogue generation aims to generate informative and meaningful responses based on both conversation context and external knowledge sources. thus far, researchers have collected knowledge-grounded dialogues for various tasks using crowdsourcing platforms, for instance, open-domain dialogues @xcite and conversational recommendation dialogues @xcite @xcite . workers are asked to base their replies on knowledge from structured knowledge bases @xcite or unstructured documents @xcite @xcite . taking advantage of recent advances in large-scale language models @xcite @xcite , researchers have also built knowledgegrounded dialogue systems by fine-tuning such language models in an end-to-end fashion @xcite @xcite . however, there are two critical challenges in these existing methods. first, it is expensive and time-intensive to collect knowledge-grounded dialogues. as shown in in this work, we present plug, a model that can unify different knowledge formats for knowledgegrounded dialogue generation. first, we convert different knowledge formats (e.g., knowledge graph, knowledge base, and passages) to unstructured text, each using a different retriever. then we use a pre-trained language model to process them into a unified representation to incorporate the knowledge into dialogue generation. we pre-train plug on different knowledge-ground dialogue corpora, including a large-scale open-domain conversation dataset from reddit. this allows plug to learn knowledge in various formats from different tasks, and thus transfer to any knowledge-grounded dialogue task with few-shot learning techniques. we evaluate the effectiveness of plug by applying it to an open-domain knowledge-grounded dialogue benchmark, wizard of wikipedia @xcite , and a knowledge-grounded conversational recommendation benchmark, redial @xcite . plug achieves results comparable to the state-of-the-art method under a fully-supervised setting. it outperforms other methods on both tasks under zero-shot and few-shot settings, demonstrating that plug can be grounded on world knowledge in different knowledge sources and generalize to different downstream tasks. our contributions are three-fold: (1) we propose a novel knowledge-based pre-trained language model, plug, that can be applied to any knowledge-grounded dialogue tasks; (2) our model achieves slightly better results than state-of-theart models in fully-supervised settings and shows promising improvements over the current stateof-the-art in zero-shot and few-shot settings; (3) we present extensive experiments to explore the bottlenecks of the task and the future direction of knowledge-grounded dialogues. ## approach we describe our approach in this section. figure 1 gives a diagram of our proposed method. we first introduce the background of knowledge-grounded dialogues and the backbone language model in section 2.1. then, we formalize the task and introduce the details of plug in section 2.2. finally, we explain the training process of our plug, which includes the pre-training dataset selection and the data pre-processing processes in section 2.3. ## experiments we demonstrate our approach on two different downstream tasks: open-domain knowledgegrounded dialogue and conversational recommendation. besides the fully-supervised learning setting, we also explore the performance of our approach in few-shot and zero-shot settings. we describe our implementation details in section a in appendix. ## related work knowledge-grounded dialogue is becoming an increasingly important topic, with datasets proposed to model its occurrence on different tasks. dialogues in these datasets are based on various for- . mats of knowledge, such as documents in opendomain conversations @xcite @xcite , movie database in movie recommendation conversations @xcite , or knowledge graph in recommendation conversations @xcite . one of the principal challenges in knowledgegrounded conversations is incorporating knowledge into dialogue systems. recent work investigates different techniques of learning a better knowledge representation to fuse knowledge in the response generation process. @xcite separately encoded the dialogue history and documents to infuse the response with external world facts. @xcite ; @xcite ; @xcite joined a knowledge graph representation in a response generation module. @xcite combined the knowledge from the database with the user intent and fed it into the decoder. unlike these studies, we use a single encoder for both dialogue context and knowledge. in order to improve the systems' performance on unseen topics and train knowledge-grounded dialogue in a low-resource setting, researchers investigate pre-training methods for the knowledgegrounded tasks. @xcite pre-trained the dialogue generation model with ungrounded dialogues and the knowledge encoder with the wikipedia dump separately. @xcite proposed a pre-trained latent variable model to learn the way that the knowledge is expressed in the response. @xcite built a document encoder and a dialogue context encoder, then pre-trained them separately in multiple stages. the knowledge encoder in these studies is pre-trained separately and only accepts the same knowledge format, while we pre-train our model with essential knowledge in the text format, thus fitting different knowledge sources in the downstream tasks. @xcite independently trained adaptors @xcite for different types of knowledge. in comparison, we use a unified essential knowledge representation in our model. zhao et al. (2020b) and guu et al. (2020) inspired by the success of pre-trained language models for a variety of natural language processing tasks @xcite @xcite , another line of work investigates learning knowledge through language models' parameters @xcite @xcite . in our pre-training process, we aim to learn extra knowledge and, more importantly, learn how to generate response grounding on the essential knowledge. two recent studies are most closely related to our work. chen et al. (2020) proposed a pre-trained model for data to text tasks. they unified the knowledge format in the pre-training data and downstream tasks, however only depend on the graph structure and do not work on knowledge-grounded dialogues. @xcite applied the document retrieval augmentation method @xcite on open-domain knowledge-grounded dialogues. however, they do not do pre-training and rely on wikipedia documents in the decoder, limiting their model to document-based dialogues. we use unified essential knowledge instead of documents in our pre-training, making our model more generalizable. our approach can be seen as generalizing both lines of work, and showing for the first time that a pre-trained model is effective for various knowledge-grounded tasks with different knowledge formats. ## conclusion and future work we present a knowledge-grounded pre-trained language model plug that can be applied to various knowledge-grounded dialogue tasks. it subsumes different knowledge sources into a simple but effective unified essential knowledge representation. evaluation results on two benchmarks indicate that our model performs better in zero-shot and fewshot settings and can generalize to different knowledge grounded tasks. as future work, we would like to augment our pre-training datasets with more knowledge sources, and apply our method to other knowledgegrounded tasks such as question answering. another interesting direction would be to develop better information retrievers since experiments show that the retriever is the main bottleneck in the knowledge-grounded dialogues.