Learning to Improve Persona Consistency in Multi-party Dialogue Generation via Text Knowledge Enhancement (2022)

Abstract: In an open-domain dialogue system, the consistent persona is a key factor to generate real and coherent dialogues. Existing methods suffer from the incomprehensive persona tags that have unique and obscure meanings to describe human’s personality. Besides, the addressee information, which is closely related to express personality in multi-party dialogues, has been neglected. In this paper, we construct a multi-party personalized dialogue dataset and propose a graph convolution network model (PersonaTKG) with addressee selecting mechanism that integrates personas, dialogue utterances, and external text knowledge in a unified graph. Extensive experiments have shown that PersonaTKG outperforms the baselines by large margins and effectively improves persona consistency in the generated responses.

## introduction endowing a dialogue agent with a consistent persona has attracted an increasing amount of research interests, as it helps to deliver a more coherent and engaging conversation for users. existing studies explore character personality through key-value persona pairs @xcite or short descriptive sentences @xcite . the key-value pairs define a few of persona categories, such as name, gender, and age, which have clear semantic meanings. the descriptive text is declarative sentences with relatively fixed patterns that introduce one's persona information such as occupation and hobbies in the first person. promising performance have been achieved on these 'well-defined' persona datasets @xcite @xcite . recently, @xcite proposed a new dialogue dataset hla-chat using tags as persona information. hla-chat was collected from scripts of hit tv dramas, and the persona tags of the characters were tropes that are determined by audiences' intuitively, the persona consistency in the generated responses of hla-chat can be further improved by incorporating external knowledge. however, most of these rare persona words could not be found in the knowledge base or commonsense base such as conceptnet @xcite and atomic @xcite . thanks to tvtropes, the audiences can contribute to the laconic and de-tailed interpretation of these tropes on this wiki website, as shown in on the other hand, most of the existing personalized response generation studies focus on bilateral dialogue @xcite . in effect, the conversations in the real world often occurs between multiple speakers, which is called multi-party conversation (mpc). mpc is generally composed of speakers, utterances, and addressees (i.e. the recipient corresponding to an utterance). the expression of persona in the dialogue is often closely related to the addressee. for example, the addressee can be a friend, a lover, a stranger, etc. for these different relationships, every speaker may have a different way of expressing their personas, so predicting the addressee can assist the dialogue system to promote the persona consistency in the responses. thus, we incorporate the addressee selecting mechanism into the response generation model and leverage a posterior selection module to improve the addressee prediction. the complex context structure of mpc urges researchers to constantly seek novel and effective context modeling methods, where graph convolution networks have already achieved promising results @xcite . however, none of the existing hierarchy @xcite , role sensitive @xcite , or gcn-based modeling methods consider the different personas of the speakers as well as the correlation between personas and utterances. in this paper, we first utilize the hierarchical recurrent encoder-decoder structure @xcite and the bidirectional gru @xcite to model the multi-turn dialogue context and the personas of all the speakers. then we build a unified graph with utterances and personas as nodes, and employ gcn to aggregate dialogue context information. our contributions are summarized as follows: ## dataset construction film and television drama scripts are a common dataset source for dialogue system research, where there are high-quality personas, distinctive characters, and many rounds of dialogues. we collects 30 english scripts from the website 3 to construct a multi-party dialogue dataset. inspired by @xcite , we employ the tropes on tvtropes as the personas of the characters, which are annotated by the audiences with more representative and distinctive meanings. for the crawled script web pages, the main preprocessing steps are as follows: (i) use regular expressions to filter out html escape characters and finally, we construct a new multi-party dialogue dataset, hla-chat++, which has 823,204 conversations with character persona annotations. according to the statistics, hla-chat++ has 239 characters in the dataset, with an average of 3,444 dialogues per character, and an average of 27,440 dialogues per tv dramas. ## model personatkg is shown in figure 1 , which is based on a seq2seq structure. the task can be formally defined as: given context @xmath0, x 2 , . . . , x m }, where x i denotes the utterance of the speaker, and persona set @xmath1, p 2 , . . . , p k }, where k denotes k speakers, p 1 is the personas of the responder and p i (@xmath2. the goal is to generate response @xmath3. . . y n based on context and persona set, where y i denotes the word generated in each step. ## conclusion in this paper, we first propose to use unstructured text knowledge in mpc to explain the incomprehensive persona tags. we construct a multi-party personalized dialogue dataset hla-chat++ based on english drama scripts and propose a model per-sonatkg with addressee selecting mechanism that integrates personas, dialogue utterances, and external text knowledge in a unified graph. the results show that the automatic and human evaluation are superior than other baselines, which demonstrate the effectiveness of our methods in improving persona consistency.