You Impress Me: Dialogue Generation via Mutual Persona Perception (2020)

Abstract: Despite the continuing efforts to improve the engagingness and consistency of chit-chat dialogue systems, the majority of current work simply focus on mimicking human-like responses, leaving understudied the aspects of modeling understanding between interlocutors. The research in cognitive science, instead, suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose Pˆ2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding. Specifically, Pˆ2 Bot incorporates mutual persona perception to enhance the quality of personalized dialogue generation. Experiments on a large public dataset, Persona-Chat, demonstrate the effectiveness of our approach, with a considerable boost over the state-of-the-art baselines across both automatic metrics and human evaluations.

## introduction thanks to the advance in neural models and the accessibility of massive datasets, open-domain dialogue (i.e. chit-chat) systems have made great progress towards mimicking human-like responses. nevertheless, there still exist some serious challenges in building personalized chatbots that can deliver engaging conversations and gain user trust @xcite . for example, current chit-chat systems tend to generate uninformative responses @xcite . moreover, they are usually lack of coherent personality traits due to the fact that training dialogues actually come from a diverse set of speakers @xcite . ## methodology overview the central idea of p 2 bot is to explicitly model understanding between interlocutors and enhance dialogue generation via mutual persona perception. it comprises two components, transmitter and receiver, respectively responsible for dialogue generation and mutual persona perception. figure 2 gives an overview of p 2 bot: interlocutor a has a persona w a , described with l profile sentences when she first meets the other interlocutor b, they are going to know each other through a n -turn dialogue ( as the conversation goes on, impressions are gradually built via utterances. for example, when a says "i don't watch movies more of a writer.", the impression that "a is a writer." is left on b's mind. as mentioned above, a successful conversation helps interlocutors know each other, which means b's impression of a should correspond to a's persona and vice versa. receiver aims to measure the proximity between the built impressions and the actual personas. specifically, as demonstrated by the dashed black lines in figure 2 , receiver first projects impressions and personas into a latent space, and then measures the relevance between them based on the impression encoding (e.g. h a , b's impression on a, projected from a's transmitter block block block block block [ps] i [sos] . [cls] block block block block block … … … … … … … … … [mask] [mask] i [eos] isnext block bought block … [mask] block i block don't position encoding w e w e w e layer 12 layer 1 … figure 3 : the overall architecture of transmitter. "block" is short for "transformer block". arrows bridge the current block to subsequent blocks of its following layer. position encoding is to incorporate position information into block by assigning an embedding for each absolute position in the sequence. here we omit the architecture inside the block, and refer the readers to @xcite for more details. ## transmitter following previous work @xcite , we treat dialogue generation as a sequence generation problem. concretely, we employ the pretraining transformer language model introduced in @xcite (i.e. gpt) to initialize transmitter. the entire training procedure consists of two steps: (1) supervised dialogue generation. we optimize transmitter via maximum likelihood estimation (mle) on the supervised dialogue generation task. (2) self-play model finetuning. we simulate dialogues between two randomly paired interlocutors, encouraging transmitter to learn a policy that maximizes reward signals via reinforcement learning (rl) @xcite . the design of the reward function considers both language modeling and our proposed mutual persona perception. ## receiver receiver is devised to measure the proximity between the built impressions and the actual personas, implemented by negative sampling. specifically, in training, we randomly sample a persona distractor w z . receiver is trained to identify the real persona w a from {w a , w z }. in inference, for each utterance, receiver is responsible for providing a reasonable relevance score, to model our proposed mutual persona perception. the score subsequently joins the self-play fine-tuning on transmitter as part of the rewards, as in equation 8 . ## experiment we conducted experiments on the dataset persona-chat, assessing p 2 bot using both automatic metrics and human evaluations. to verify the effectiveness of our proposed mutual persona perception, we perform a thorough model analysis in section 5.3. finally, we probe receiver's capability on perceiving persona in section 5.4. ## related work methods to build open-domain dialogue systems generally fall into two major categories: retrievalbased and generative-based. retrieval-based methods retrieve response candidates and rank them based on the matching scores with the dialogue @xcite @xcite . generative-based methods typically use seq2seq model as the backbone @xcite @xcite , where the encoder extracts the information in an utterance and the decoder generates the response. our work adopts a similar architecture. besides supervised learning, researchers also explore reinforcement learning based methods. @xcite applied reinforcement learning for negotiation dialogues and showed it outperforms supervised learning when negotiating with humans. @xcite proposed to generate dialogue responses by dual learning based domain adaptation. @xcite built a coherence model to provide the reward signal for penalizing dull responses. @xcite employed reinfrocement learning to learn an intermediate structure span. our approach differs from this line of work in that we focus on improving personalized dialogues via mutual persona perception, which has not yet been explored before. more recently, under the topic of dialogue personalizing, @xcite sha (2018 furthermore, researchers explored to generate diverse responses conditioned on persona @xcite . personalization in goal-oriented di-alogue systems has also received some attention @xcite . the researches focus more on making the goal-oriented bots adjust the response according to different user profiles, while we aim to endow bots with persistent personalities. ## conclusion & future work we propose p 2 bot, a transmitter-receiver framework which explicitly models understanding between interlocutors. under this framework, mutual persona perception is incorporated as a reward signal to achieve the personalized dialogue generation. experiments on a large public dataset persona-chat demonstrate the effectiveness of our approach. for future work, we would like to extend receiver to conversational recommender systems. after turns of chatting, the agent should be able to infer the user's persona, based on which personalized contents can be recommended.