Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness (2020)

Abstract: We explore the task of improving persona consistency of dialogue agents. Recent models tackling consistency often train with additional Natural Language Inference (NLI) labels or attach trained extra modules to the generative agent for maintaining consistency. However, such additional labels and training can be demanding. Also, we find even the best-performing persona-based agents are insensitive to contradictory words. Inspired by social cognition and pragmatics, we endow existing dialogue agents with public self-consciousness on the fly through an imaginary listener. Our approach, based on the Rational Speech Acts framework (Frank and Goodman, 2012), can enforce dialogue agents to refrain from uttering contradiction. We further extend the framework by learning the distractor selection, which has been usually done manually or randomly. Results on Dialogue NLI (Welleck et al., 2019) and PersonaChat (Zhang et al., 2018) dataset show that our approach reduces contradiction and improves consistency of existing dialogue models. Moreover, we show that it can be generalized to improve context-consistency beyond persona in dialogues.

## introduction in the study of dialogue agents, consistency has been a long-standing issue. to resolve this, much research has been conducted to endow dialogue agents with personas. @xcite propose to encode persona in embeddings and @xcite introduce a persona-conditioned dialogue dataset. on top of these works, many efforts have been made to improve consistency. in spite of such recent significant progress, there is much room for improving persona-based dialogue agents. we observe that even the best performing persona-based generative models @xcite @xcite i like to stay at home. ## insensitivity to contradictory words in existing persona-based agents although conditional language generation has shown promising progress, maintaining consistency within the generation yet remains unsolved. from quantitative evaluation, we reveal existing generative models for dialogues are highly insensitive to contradictory words. dialogue nli evaluation. @xcite introduce the dialogue nli dataset based on the personachat dataset @xcite . they collect entailing and contradictory utterances to the given persona, and release an evaluation set comprised of dialogues each with 31 utterance candidates: 10 entailing, 10 neutral, and 10 contradictory utterances with 1 ground-truth (gt) utterance. on this evaluation set, we run three recent models @xcite ; roller persona i love wearing skinny jeans and shirts. i am a blonde girl with short hair. gt utterance (i, 1.87) (have, 51.42) (really, 201.45) (short, 1.78) (hair, 1.30) (and, 2.81) (it, 45.25) (is, 2.19) (blonde, 461.60). contradict@1-utt (what, 60.89) (color, 103.11) (is, 1.99) (your, 1.06) (hair, 1.05) (?, 1.11) (mine, 3.57) (is, 1.03) (brown, 17.25). figure 2 shows that all three models select contradictory candidates much more often than the gt utterances (see further results in ## approach we introduce how to endow dialogue agents with public self-consciousness, which helps them keep consistency in mind at each generation step by reflecting an imaginary listener's distribution over personas. since the imaginary listener arises from the plain dialogue-agent, separate training is not needed. figure 3 illustrates its overall structure. we present how to model public selfconsciousness using the rational speech acts (rsa) framework @xcite in section 4.1. we then discuss learning of distractor selection as our major novelty for the rsa in section 4.2. ## experiments we show that our self-conscious framework can significantly improve consistency and accuracy of state-of-the-art persona-based agents on two benchmark datasets. we prove its effectiveness using both automatic and human evaluations. we also show our framework can be generalized to improve consistency of dialogue context beyond persona. ## conclusion this work investigated how modeling public selfconsciousness can help dialogue agents improve persona-consistency. we showed existing dialogue agents are highly insensitive to contradiction, and introduced an orthogonally applicable method using the rsa framework @xcite to alleviate the issue. we also designed a persona i've 5 cats. i am a construction worker. my cats are very special to me. i enjoy building houses. (𝛼 = 0) (𝛼 = 2) (𝛼 = 8) (𝛼 = 10) i'm a construction worker. i'm going to be a vet. i work construction. i'm a construction worker. construction work is great. i build houses for my cats. construction workers earn 5 cats so building houses affords us special pets. yours? kittens! d ou figure 6: an example of utterance changes by controlling the speaker rationality α on the personachat.