Stylistic Response Generation by Controlling Personality Traits and Intent (2022)

Abstract: Personality traits influence human actions and thoughts, which is manifested in day to day conversations. Although glimpses of personality traits are observable in existing open domain conversation corpora, leveraging generic language modelling for response generation overlooks the interlocutor idiosyncrasies, resulting in non-customizable personality agnostic responses. With the motivation of enabling stylistically configurable response generators, in this paper we experiment with end-to-end mechanisms to ground neural response generators based on both (i) interlocutor Big-5 personality traits, and (ii) discourse intent as stylistic control codes. Since most of the existing large scale open domain chat corpora do not include Big-5 personality traits and discourse intent, we employ automatic annotation schemes to enrich the corpora with noisy estimates of personality and intent annotations, and further assess the impact of using such features as control codes for response generation using automatic evaluation metrics, ablation studies and human judgement. Our experiments illustrate the effectiveness of this strategy resulting in improvements to existing benchmarks. Additionally, we yield two silver standard annotated corpora with intents and personality traits annotated, which can be of use to the research community.

## introduction recent years have witnessed a growth in neural methods for language modelling, specifically in the domain of open domain dialogue and interactive systems. large neural language models with billions of parameters, trained on one or more dialogue corpora, have accomplished state-of-the-art results in response generation tasks @xcite . incorporating such generators in their pipelines, end-to-end dialogue systems in alexa prize @xcite @xcite have demonstrated capabilities of engaging in prolonged live conversations with humans on a multitude of real world topics, thus bettering human-computer interaction, and paving a way for more human centered nlp applications. although such language models are capable of generating human-like responses, they often come with their own set of predicaments. leveraging only textual data sans any other explicit control mechanism for training, such models often engender undesirable responses, diminishing the trust of users in such systems. @xcite discusses the issue of knowledge hallucinations in response generation and the importance of grounding factual responses to the correct knowledge, @xcite elucidates the inconsistent and self-contradictory nature of such models, and @xcite discusses the impact of such undesirable responses in production grade human centered systems. however, in many applications it is also desirable for generators to control the style of an utterance along with its content, which is difficult to achieve using vanilla language modelling. with the motivation of incorporating more stylistic control in conversational systems, we experiment with ways of enhancing language modelling by incorporating personality and dialogue intent for controlling the mannerism and intention of the response. personality is the most fundamental dimension of variation between humans @xcite . not only does it play a crucial role in how humans react to different scenarios, but also reflects characteristic patterns of thoughts, feelings, expressions, and behaviors. speech being the ultimate form of expression is influenced by a person's personality trait @xcite . for example, the response to the query inquiring about new year's eve plans in figure 1 is not only subjective, but also dependent on the personality of the interlocutor. had the interlocutor been introverted, the response could have been different. apart from personality, the response to a query is also greatly influenced by the intentions of the interlocutors. in the same example, responding with the intention of asking subjective question would yield a different response, albeit still exhibiting the extroverted personality trait. although relying solely on language modelling might engender informative and factual response, the style and intention exuded by such generated responses are often generic and unpredictable. for controlling the response style in terms of personality and intent, we utilize control codes based on the well established big 5 personality traits taxonomy @xcite and diverse locutionary acts @xcite . ## task our goal is to experiment with ways of controlling the style of language model generated responses, using personality trait and dialogue intent based control codes. for our purpose, we utilize the big-5 personality traits listed in type control code abbreviation description possible levels big-5 personality traits agreeableness agr level of critical and rational nature. strong/weak openness opn level of imagination and insight. strong/weak conscientiousness con level of self-discipline and efficiency. strong/weak extraversion ext level of outgoing nature. strong/weak neuroticism neu tendency to experience negative emotions. strong/weak corpus based traits attitude overall pre-dominant stance of an interlocutor. positive/negative/neutral tone overall pre-dominant intention of an interlocutor. subjective/objective/both length response length preference of an interlocutor. talkative/reserved intent subjectivity subj intention of sharing personal anecdotes or opinions. present/absent objectivity obj intention of sharing factual knowledge. present/absent subjective question subj q intention of seeking personal anecdotes or opinions. present/absent objective question obj q intention of seeking factual knowledge. present/absent ## data we leverage the publicly available multi-turn, large scale wizard of wikipedia @xcite , and topical chat @xcite ) corpora for our experiments, which we further enrich with turn wise intent and personality trait annotations. ## modelling mathematically, given a response y consisting of tokens (y 1 , ..., y n ), and the conversation context till the current turn c, language modelling for response generation estimates p(y |c). employing personality trait p , intent control codes i, and the relevant facts f , we model the posterior probability distribution p(y |c, p, i, f ). further, in order to facilitate learning we incorporate a multi-task learning framework, where along with generating the response y , we perform fact selection and target personality p and intent control code i prediction. we employ parameterized neural networks, and train end-to-end leveraging encoder-decoder transformers @xcite bart @xcite and blenderbot @xcite as the base architectures of our model. figure 2 illustrates the end-to-end system, and below we detail each component. foot_0 ## conclusion here we experiment with training end-to-end methods for controlling the response style in generative conversational models. we believe incorporating such methods in human facing dialogue systems should benefit the system by providing it with more control. using combinations of big-5 personality traits and dialogue intent based stylistic control codes during language modelling, we are able to successfully control the style of a response as desired, the efficacy of which is further established by the achieved results. additionally, we engender two annotated dialogue corpora with intents and personality traits for use by the community. thomas wolf, lysandre debut, victor sanh, julien chaumond, clement delangue, anthony moi, pierric cistac, tim rault, remi louf, morgan funtowicz, joe davison, sam shleifer, patrick von platen, clara ma, yacine jernite, julien plu, canwen xu, teven le scao, sylvain gugger, mariama drame, quentin lhoest, and alexander rush. 2020. transformers: state-of-the-art natural language processing. in proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations, pages 38-45, online. association for computational linguistics. thomas wolf, victor sanh, julien chaumond, and clement delangue. 2019. transfertransfo: a transfer learning approach for neural network based conversational agents. jing xu, arthur szlam, and jason weston. 2021. beyond goldfish memory: long-term open-domain conversation.