Harmonizing Code-mixed Conversations: Personality-assisted Code-mixed Response Generation in Dialogues (2024)

Abstract: Code-mixing, the blending of multiple languages within a single conversation, introduces a distinctive challenge, particularly in the context of response generation. Capturing the intricacies of code-mixing proves to be a formidable task, given the wide-ranging variations influenced by individual speaking styles and cultural backgrounds. In this study, we explore response generation within code-mixed conversations. We introduce a novel approach centered on harnessing the Big Five personality traits acquired in an unsupervised manner from the conversations to bolster the performance of response generation. These inferred personality attributes are seamlessly woven into the fabric of the dialogue context, using a novel fusion mechanism, . It uses an effective two-step attention formulation to fuse the dialogue and personality information. This fusion not only enhances the contextual relevance of generated responses but also elevates the overall performance of the model. Our experimental results, grounded in a dataset comprising of multi-party Hindi-English code-mix conversations, highlight the substantial advantages offered by personality-infused models over their conventional counterparts. This is evident in the increase observed in ROUGE and BLUE scores for the response generation task when the identified personality is seamlessly integrated into the dialogue context. Qualitative assessment for personality identification and response generation aligns well with our quantitative results.

## introduction conversations foot_0 serve as the primary medium for exchanging ideas and cultivating acquaintance among individuals @xcite . remarkably, many people exhibit fluency in multiple languages, seamlessly blending these linguistic resources in their daily communications @xcite . this phenomenon, characterized by fusing distinct languages to convey meaning, is referred to as code-mixing. while code-mixing prevails as a widespread linguistic phenomenon @xcite , it has not garnered significant attention within the mainstream nlp community, where monolingual text processing has been the predominant focus. of late, there is a growing recognition of the critical importance of comprehending code-mixed conversations resulting in an increased number of studies investigating diverse aspects of code-mixing in conversations @xcite @xcite , such as the identification of humor @xcite @xcite , emotional expression @xcite , and sarcasm @xcite . however, the realm of response generation within code-mixed dialogues remains an underexplored frontier @xcite . to this end, we propose tackling the response generation challenge for code-mixed conversations. it is crucial to note that while response generation is a vital avenue to explore, it diverges significantly from conventional natural language under-standing tasks since a uniform, 'one-size-fits-all' model proves inherently inadequate in this context @xcite . every individual possesses a unique set of preferences and life experiences, which collectively mould their distinct personalities, subsequently exerting a profound influence on their responses to identical queries @xcite . figure 1 illustrates this point. as evident, the response to a seemingly straightforward question, such as "would you like to accompany me to a party?", can differ based on the listener's prominent personality traits. interlocutor a, characterized as an neurotic, responds distinctively compared to interlocutor b, who leans more towards being extrovert. appendix a.1 presents the definition of personality traits, along with examples. personality traits, by their very nature, span a vast spectrum and thus possess the potential for infinite possibilities @xcite . numerous studies have been conducted to quantify these traits @xcite @xcite , with the big five personality traits @xcite emerging as the prominent framework in this context. this theory distils human personality into five distinctive dimensions: openness (opn), conscientiousness (con), extraversion (ext), agreeableness (agr), and neuroticism (neu), in which each dimension encapsulates a pivotal facet of an individual's character. for instance, elevated levels of openness may signify a predisposition towards imagination. here, we adopt this widely accepted model as the foundation for characterizing a speaker's personality. our central hypothesis contends that incorporating personality indicators within the response generation process plays a pivotal role in generating contextually appropriate responses to given queries. given the intricate and non-generalizable nature of manually annotating personality traits, we propose an unsupervised learning approach to acquire these traits, which, in turn, enhances response generation capabilities. in a nutshell, our contributions are four-fold: 1. we ## problem definition the complete problem definition can be divided into two phases as follows: phase 1: speaker personality detection. given the contextual utterances (s 1 , u 1 ), (s 2 , u 2 ), . . . , (s n-1 , u n-1 ) such that utterance u i is uttered by speaker s j , we aim to generate personality p n for speaker s n . a classification model selects p n , such that p @xmath0, con, ext, agr, neu}, and maps the selected trait class into a templatic personality defining the speaker (c.f. ## dataset datasets for code-mixed conversations are inadequate, especially for multi-turn, multi-party conversations. in this study, we consider the masac dataset @xcite , containing hindi-english code-mixed discourse of multi-turn and multi-party nature from an indian tv series 3 . the dataset was originally curated to perform the task of sarcasm and humour detection since it contains conversations similar to daily discourse among peers. ## proposed methodology in this section, we discuss our proposed methodology, with the foremost objective being the effective identification of personality attributes from the dialogue context. to achieve this, we propose an unsupervised technique that leverages response generation performance to improve personality identification. subsequently, we fuse the personality attributes into the dialogue context to generate responses influenced by individual traits. we propose the incorporation of an intermediary module within the core encoder. this module leverages a straightforward yet effective two-step attention mechanism, facilitating the fusion of personality attributes with the representation of the dialogue. broadly, we employ context-aware attention @xcite , which is instrumental in infusing personality characteristics into the key and value vectors of the dialogue. subsequently, we employ axial attention @xcite to yield a refined, conclusive representation, which ultimately feeds into the decoder. figure 4 provides a schematic diagram of our model. in the following subsections, we offer a comprehensive overview of individual modules. ## conclusion we explored the task of utilising speaker personalities to aid response generation in the domain of code-mixed dialogues. speaker personalities, from the big five personality traits, are learned in an unsupervised manner an incorporated with dialogue context using a novel fusion mechanism. we leverage a two-level attention mechanism employing context aware and axial attention approaches to efficiently fuse the personality information with dialogue context. our experiments demonstrated a notable improvement in response quality and coherence when personality information is fused into the systems. furthermore, we provided insights into the inferred personality traits and their qualitative connection to response generation.