Augmenting Knowledge-grounded Conversations with Sequential Knowledge Transition (2021)

Abstract: Knowledge data are massive and widespread in the real-world, which can serve as good external sources to enrich conversations. However, in knowledge-grounded conversations, current models still lack the fine-grained control over knowledge selection and integration with dialogues, which finally leads to the knowledge-irrelevant response generation problems: 1) knowledge selection merely relies on the dialogue context, ignoring the inherent knowledge transitions along with conversation flows; 2) the models often over-fit during training, resulting with incoherent response by referring to unrelated tokens from specific knowledge content in the testing phase; 3) although response is generated upon the dialogue history and knowledge, the models often tend to overlook the selected knowledge, and hence generates knowledge-irrelevant response. To address these problems, we proposed to explicitly model the knowledge transition in sequential multi-turn conversations by abstracting knowledge into topic tags. Besides, to fully utilizing the selected knowledge in generative process, we propose pre-training a knowledge-aware response generator to pay more attention on the selected knowledge. In particular, a sequential knowledge transition model equipped with a pre-trained knowledge-aware response generator (SKT-KG) formulates the high-level knowledge transition and fully utilizes the limited knowledge data. Experimental results on both structured and unstructured knowledge-grounded dialogue benchmarks indicate that our model achieves better performance over baseline models.

## introduction knowledge-grounded conversations @xcite @xcite , aiming at improving the informativeness * work done at data science lab, jd.com. ## related work recently, dialogue systems have gained more attention in both research community @xcite @xcite @xcite and industry @xcite , because of its practicality in the real application, such as chatbot and customer services @xcite @xcite @xcite . with external knowledge sources, dialogue systems can generate more specific and informative response, which has great potential to resolve the common response problem @xcite . the majority of previous works decomposed the pre-training knowledge generator phase fine-tuning generation phase klg klg context context tag tag klg klg context context tag tag transformer block n-1 transformer block n transformer block n-1 transformer block n transformer block n ffn & softmax nllloss transformer block n ffn & softmax output input representation transformer block 1 transformer block 2 transformer block n ... t 1 t 2 t n s t ... sequential knowledge transition phase knowledge-grounded dialogue generation task into two sub-problems: knowledge selection and response selection. in knowledge selection, previous works proposed to use the keyword matching @xcite , information retrieval @xcite and entity diffusion @xcite methods to detect the relevant knowledge based on dialogue context, and finally feed both dialogue utterances and the selected knowledge into generative models. specifically, @xcite proposed to employ the graph attention mechanism to encode the retrieved relevant knowledge graph, which can augment the semantic understanding of dialogue context. @xcite proposed to use the prior and posterior distributions over knowledge to facilitate knowledge selection. although these work are capable to model the relationship between context and knowledge, they still ignored the knowledge transition characteristic, which is important for knowledge selection. human dialogue depends on both local information and global information. peng et al. (2019) also pointed out that natural language understanding requires a coherent understanding of a series of events or actions, not only what events have appeared, but also what is likely to happen next. therefore, it is critical to obtain the natural and relevant knowledge for the knowledge-grounded dialogue generation. @xcite proposed to recurrently update the knowledge based on conversation history and progressively incorporate it into the history step-by-step. but they only consider the relationship of history to knowledge. however, these models may also suffer from a knowledge sparse problem, due to the low-resource limitation in reality @xcite . in reality, sufficient knowledge-grounded dialogues data are difficult to obtain. to tackle this practical challenge, @xcite proposed to augment the dialogue generation with external non-conversational text, which may also introduce much noise. @xcite proposed to pre-train the knowledge encoder with unstructured knowledge and fine-tune the model using the limited knowledge-grounded training examples. in our work, we propose to make full use of our training data and model the high-level knowledge transition process, which can resolve the sparse problem in knowledge-grounded dialogue data. ## approach in this section, we propose a novel sequential knowledge transition model with pre-trained knowledge-aware response generator (skt-kg), as shown in figure 2 . this model contains three major parts: pre-trained knowledge-aware response generator, sequential knowledge transition, and transformer decoder. specifically, we firstly pretrain a transformer-based knowledge-aware response generator based on the knowledge and its corresponding natural sentence. and then, we utilize a bilstm-crf @xcite network to model the knowledge transition process, and select knowledge tag with maximum score and its corresponding knowledge content. finally, we feed the context utterances and this selected knowledge content into the knowledge-aware response generator to fine-tune it. after fine-tuning, response can be generated by given the selected knowledge tag and corresponding content, and history dialogue utterances. figure 3 : an example for the input representation. in the pre-training phase, we mask the context utterances part. and for the fine-tune response generation phase, we concatenate the selected knowledge tag, the selected knowledge content and history utterances as input. ## conclusion and future work in this paper, we propose a sequential knowledge transition model with knowledge-aware response generator to model the high-level knowledge transition and fully utilize the low-resource knowledge data. skt-kg models can abstract knowledge into tags which leads our model easily to apply into both the structured and unstructured knowledgegrounded conversations. besides, we propose a pre-trained knowledge-aware response generator, aiming at generating a natural sentence based on a given knowledge, to make full use of the limited data. experimental results on both structured and unstructured knowledge-grounded dialogue datasets show that our skt-kg model outperforms baseline models. as for future work, we intend to apply variational autoencoder to unstructured dataset, in order to empower models to learn the knowledge topic by themselves.