ABLE : Personalized Disability Support with Politeness and Empathy Integration (2024)

Abstract: In today’s dynamic world, providing inclusive and personalized support for individuals with physical disabilities is imperative. With diverse needs and preferences, tailored assistance according to user personas is crucial. In this paper, we introduce ABLE (Adaptive, Bespoke, Listen and Empathetic), a Conversational Support System for Physical Disabilities. By tracking user personas, including gender, age, and personality traits based on the OCEAN model, ABLE ensures that support interactions are uniquely tailored to each user’s characteristics and preferences. Moreover, integrating politeness and empathy levels in responses enhances user satisfaction and engagement, fostering a supportive and respectful environment. The development of ABLE involves compiling a comprehensive conversational dataset enriched with user profile annotations. Leveraging reinforcement learning techniques and diverse reward mechanisms, ABLE trains a model to generate responses aligned with individual user profiles while maintaining appropriate levels of politeness and empathy. Based on rigorous empirical analysis encompassing automatic and human evaluation metrics based on persona-consistency, politeness accuracy, empathy accuracy, perplexity, and conversation coherence, the efficacy of ABLE is assessed. Our findings underscore ABLE’s success in delivering tailored support to individuals grappling with physical disabilities. To the best of our knowledge, this is the very first attempt towards building a user’s persona-oriented physical disability support system.

## introduction physical disabilities present significant challenges to individuals, affecting their daily activities and quality of life. according to the world health organization (who), over a billion people, approximately 15% of the global population, live * equal contribution. 1 dataset and codes can be accessed at emnlp2024-able with some form of disability @xcite . providing effective support for individuals with physical disabilities is crucial in enabling them to navigate their environment, engage in social interactions, and lead fulfilling lives. conventional support systems @xcite tried to address this issue but lacked in fulfilling the diverse needs of this population in facilitating independence, mobility, and access to different resources. personalization is the key in providing effective support for individuals with physical disabilities. each user may have distinct characteristics, preferences, and requirements, necessitating tailored solutions @xcite . research indicates that gender can influence the experience and perception of disability, with women often facing unique challenges related to societal expectations and access to healthcare @xcite . individuals may exhibit variations in gender, age, and personality traits, such as those identified in the ocean model @xcite . moreover, meta-communicative aspects, including politeness and empathy, are integral in fostering effective communication and rapport with users @xcite . tailoring support to individual needs and preferences enhances user satisfaction, engagement, and outcomes. despite the critical role of support systems, current solutions frequently fail to address the diverse needs of individuals with physical disabilities adequately. many existing systems rely on standardized, generic responses, lacking the necessary personalization and adaptability to cater to individual user characteristics @xcite . furthermore, the absence of politeness and empathy in these interactions often leads to impersonal and ineffective communication, ultimately hampering user engagement and satisfaction @xcite . the rapid advancements in technology un-derscore the urgent demand for more personalized and empathetic support solutions that are finely attuned to the distinct needs and preferences of users. focusing on these shortcomings, we propose able: an adaptive, bespoke, listen, and empathetic conversational support system tailored specifically for individuals with physical disabilities, aiming to provide personalized assistance. we begin with the creation of a large-scale persona-tailored physical disability support conversational dataset, perpdscd, which encompasses various disabilities and support issues. leveraging perpdscd, able is developed in a reinforcement learning framework, where novel rewards are strategically designed to guide its learning process. these rewards guide able to generate personalized responses that align with individual user profiles while incorporating politeness and empathy cues. the effectiveness of able is assessed through rigorous automatic and human evaluation, focusing on measures, such as persona-consistency, genderage consistency, politeness correctness, empathy correctness, linguistic fluency, and conversational coherence, to ensure its robustness and efficacy in providing tailored support. our key contributions include: ## related work in the domain of physical disability support, historical developments lay the groundwork for understanding the current landscape. initial attempts focused on rudimentary assistive technologies and human-centered interventions @xcite . in recent years, the importance of providing effective support for individuals with physical disabilities through conversational systems has been emphasized by numerous studies in healthcare @xcite . several studies underscore the significance of facilitating conversations tailored to the specific needs and preferences of users with disabilities @xcite @xcite . the shift towards personalized support systems for individuals with physical disabilities parallels advancements in healthcare and technology. with the advent of artificial intelligence (ai)-driven conversational agents, there is a growing recognition of the need for tailored assistance in this population @xcite . the trajectory outlined by @xcite and @xcite in mental health counseling sets the stage for the application of personalized conversational agents in healthcare. as discussed by @xcite , the systematic review sheds light on the potential of personalized systems to enhance patient outcomes and engagement. personalization in physical disability conversations has been highlighted as a crucial factor for enhancing user engagement and satisfaction @xcite . moreover, the incorporation of politeness and empathy in support interactions has been shown to improve user experience and foster a supportive environment significantly @xcite . recent studies have also addressed the importance of incorporating politeness and empathy in conversational systems. techniques, such as reinforcement learning have been employed to adapt the politeness and empathy levels of system responses @xcite @xcite @xcite @xcite . however, these approaches have not been extensively applied in the context of physical disability support conversations. conversational systems for healthcare vary widely in applications, from behavior change interventions @xcite , for chronic conditions @xcite to aiding cognitive disabilities @xcite . personalized agents empower diverse population, from adolescents with autism spectrum disorder @xcite to older adults promoting physical activity @xcite . despite advancements, challenges persist: lack of personalization, empathy, and reliance on rule-based models @xcite @xcite . further, conversational systems explicitly tailored for supporting individuals with physical disabilities remain under-explored @xcite . while recent research has explored the integration of personality traits, such as those defined by the ocean model, in conversational systems @xcite @xcite , these works often focus on fixed personas, neglecting the variability and complexity of individual personalities exhibited during conversations @xcite . therefore, to build a support system for physical disabilities, we introduce able which utilizes a comprehensive novel conversational dataset named perpdscd, comprising 18,026 dialogues enriched with user profiles and annotations, to offer tailored support interactions. using reward different functions, able generates responses aligned with individual user profiles while maintaining appropriate levels of politeness and empathy. our approach represents a significant advancement in the field, addressing the shortcomings of existing systems and paving the way for further research in this important domain. to the best of our knowledge, perpdscd and able constitute the first attempt to create a large-scale dataset and conversational system, specifically tailored for supporting individuals with physical disabilities. ## resource creation we create a large-scale physical disability support conversational dataset perpdscd consisting of personalized support conversations with the user's gender, age, and persona. the perpdscd addresses a wide array of challenges related to physical disabilities, viz. mobility aids, home modifications, physical therapy exercises, assistive technology, pain management, activities of daily living (adls), emotional support, employment and education, social interaction, fitness and recreation, peer support groups, parenting with disabilities, and transitions and life changes. it delves into specific issues for these disabilities, such as mobility impairments, visual impairments, hearing impairments, speech impairments, neu-rological disorders, spinal cord injuries, amputations, orthopedic disabilities, cerebral palsy, muscular dystrophy, balance and gait disorders, chronic pain, aging-related disabilities, and mental health conditions. the details of each of the disabilities and respective issues can be found in ## experiments due to space restrictions, implementation details of all the models are given in section a.2 of the appendix. ## results and analysis automatic evaluations: in examining task-specific metrics: p ca, gaa, p a, and ea, a discernible pattern is seen i.e. gpt2-@xmath1, llama2-7b <, mistral-7b <, zephyr-7b < phi-1.5 < pdss ≈ able-@xmath2. notably, pdss and able-r exhibit similar performance, attributed to able's initialization from p ds θ . it can observed that llama2-7b, mistral-7b, zephyr-7b, and phi-3 are outperformed by both able-tr, and able-gr which suggests that we do need rl to steer the model towards persona-consistent supportive dialogues. the better performance of able-gr can be traced back to the influence of r 1 , r 2 , r 3 , and r 4 , underscoring the pivotal role of persona, gender, age, politeness, and empathy in guiding able to formulate persona-consistent, polite, and compassionate responses. moreover, able's success across all metrics can be attributed to its assimilation of patient profile information and adept adaptation of politeness and empathy levels. the integration of task-specific rewards aids able in approximating a more precise distribution, further enhancing its competitive edge over the eight baselines. the inclusion of responsequality rewards fosters a dynamic rapport between the system and the user, enabling able to focus on pertinent details and craft refined responses. this results in better language understanding ability to generate contextually relevant, diverse, and engaging responses. this underscores the dual necessity of all six rewards in yielding responses of elevated quality, validating our initial hypothesis. generated responses of different models are illustrated in figure 5 . both automatic and human evaluations validate able's efficacy in delivering high-quality conversational support to individuals with physical disabilities, suggesting its potential to significantly enhance user experience and overall well-being. ## error analysis while the results of our empirical analysis demonstrate the overall effectiveness of able in delivering tailored support to individuals with physical disabilities, areas for improvement can be identified. one notable aspect of error stems from the misalignment between user personas and the generated responses. despite our efforts to track user characteristics, there are instances where the generated responses do not fully align with the identified personas. this discrepancy may be attributed to the complexity of human personality traits and the inherent challenges in accurately capturing and representing them in the conversational dataset. additionally, we observed instances of suboptimal politeness and empathy levels in certain responses, which can lead to decreased user satisfaction and engagement. while able integrates politeness and empathy levels into its response generation process, further refinement is needed to ensure consistency and appropriateness across all interactions. furthermore, variations in conversation coherence were noted in some interactions, resulting in disjointed or fragmented dialogue flow. this may be attributed to limitations in the training data or deficiencies in the model's ability to capture and maintain context over extended conversations. ## conclusion in this paper, we introduce able (adaptive, bespoke, listen and empathetic), a conversational support system tailored for individuals with physical disabilities. able leverages user personas based on the ocean model to provide personalized assistance, integrating politeness and empathy to enhance user satisfaction. first, a physical disability support conversational dataset perpdscd with user profile annotations is curated. then leveraging diverse rewards, able effectively generates responses aligned with individual user profiles while maintaining appropriate levels of politeness and empathy. through empirical analysis of the evaluation results, we demonstrate that able's efficacy in delivering tailored support for individuals with physical disabilities. this study represents a significant step towards building user personaoriented physical disability support systems and sets a foundation for further research in this domain. future work could explore enhancements to able's architecture, incorporate additional user profile factors, and extend its applicability to other domains beyond physical disabilities.