Individuals living with disabilities often face challenges in their daily lives, from managing physical tasks to coping with emotional needs. It is imperative to provide them with personalized, courteous, and empathetic support that can address their unique needs. To bridge this gap, we propose an Empathetic Disability Support System (EDiSS), designed to offer personalized support tailored with correct politeness and empathetic strategies as per individual users’ OCEAN traits, gender, and age. To train EDiSS, first, a specialized personalized disability support dialogue dataset (PDCARE) is created encompassing a wide spectrum of disabilities, such as Spinal Cord Injuries, Neurological Disorders, Orthopedic Disabilities, etc, and support areas like Physical Therapy Exercises, Pain Management, Emotional Support, etc. EDiSS employs a reinforcement learning-based dialogue model with a novel reward function. It adapts its tone and content based on the user’s persona, gender, and age to provide respectful and empathetic assistance across various aspects of daily living. Our experiments and evaluation demonstrate the effectiveness of EDiSS in improving the quality of life of individuals with disabilities, marking a significant advancement in leveraging technology to provide much-needed support and assistance in their daily challenges.

In medical question-answering, traditional knowledge triples often fail due to superfluous data and their inability to capture complex relationships between symptoms and treatments across diseases. This limits models’ ability to provide accurate, contextually relevant responses. To overcome this, we introduce MedEx, which employs First-Order Logic (FOL)-based reasoning to model intricate relationships between diseases and treatments. We construct FOL-based triplets that encode the interplay of symptoms, diseases, and treatments, capturing not only surface-level data but also the logical constraints of the medical domain. MedEx encodes the discourse (questions and context) using a transformer-based unit, enhancing context comprehension. These encodings are processed by a Knowledge Injection Cell that integrates knowledge graph triples via a Graph Attention Network. The Logic Fusion Cell then combines medical-specific logical rule triples (e.g., co-occurrence, causation, diagnosis) with knowledge triples and extracts answers through a feed-forward layer. Our analysis demonstrates MedEx’s effectiveness and generalization across medical question-answering tasks. By merging logical reasoning with knowledge, MedEx provides precise medical answers and adapts its logical rules based on training data nuances.

In the realm of multi-armed bandit problems, the Gittins index policy is known to be optimal in maximizing the expected total discounted reward obtained from pulling the Markovian arms. In most realistic scenarios however, the Markovian state transition probabilities are unknown and therefore the Gittins indices cannot be computed. One can then resort to reinforcement learning (RL) algorithms that explore the state space to learn these indices while exploiting to maximize the reward collected. In this work, we propose tabular (QGI) and Deep RL (DGN) algorithms for learning the Gittins index that are based on the retirement formulation for the multi-armed bandit problem. When compared with existing RL algorithms that learn the Gittins index, our algorithms have a lower run time, require less storage space (small Q-table size in QGI and smaller replay buffer in DGN), and illustrate better empirical convergence to the Gittins index. This makes our algorithm well suited for problems with large state spaces and is a viable alternative to existing methods. As a key application, we demonstrate the use of our algorithms in minimizing the mean flowtime in a job scheduling problem when jobs are available in batches and have an unknown service time distribution.

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 personaconsistency, 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 1 .

In a persuasive conversation forsocial good, even the most compelling and persuasive argument may fail to persuade a persuadee resisting the persuasion. Whereas use of polite tone, apologetic expressions, or deferential modes of reference such as, ‘thank you’, ‘Please’ etc. can make the conversation more interesting, engaging, and persuading to the persuadee. We propose a resistance handling polite persuasive dialogue system (Re-Po-PDS), harnessing an efficient reward function consisting of persuasiveness, politeness, coherence, and non-repetitiveness rewards in a reinforcement learning framework and a politeness transfer model. Due to the lack of polite annotated persuasive data, we first annotate thePersuaionForGooddataset with different politeness labels and name it as PP4G dataset. Then we train two transformer-based persuasive and politeness classifiers to receive persuasive and politeness feedback for our RL-agent. Further, we train a politeness transfer model which is used at the inference time as per persuadee's resistive strategy encountered to form a more polite response. Our experimental results confirm that our proposed model increases the rate of generating polite persuasive responses as compared to the available state-of-the-art dialogue models while also making the dialogues more engaging and retaining.

The potential for conversational agents offering mental health and legal counseling in an autonomous, interactive, and vitally accessible environment is getting highlighted due to the increased access to information through the internet and mobile devices. A counseling conversational agent should be able to offer higher engagement mimicking the real-time counseling sessions. The ability to empathize or comprehend and feel another person’s emotions and experiences is a crucial quality that promotes effective therapeutic bonding and rapport-building. Further, the use of polite encoded language in the counseling reflects the nobility and creates a familiar, warm, and comfortable atmosphere to resolve human issues. Therefore, focusing on these two aspects, we propose a Polite and Empathetic Mental Health and Legal Counseling Dialogue System (Po-Em-MHLCDS) for the victims of crimes. To build Po-Em-MHLCDS, we first create a Mental Health and Legal Counseling Dataset (MHLCD) by recruiting six employees who are asked to converse with each other, acting as a victim and the agent interchangeably following a fixed stated guidelines. Second, the MHLCD dataset is annotated with three informative labels, viz. counseling strategies, politeness, and empathy. Lastly, we train the Po-Em-MHLCDS in a reinforcement learning framework by designing an efficient and effective reward function to reinforce correct counseling strategy, politeness and empathy while maintaining contextual-coherence and non-repetitiveness in the generated responses. Our extensive automatic and human evaluation demonstrate the strength of the proposed system. Codes and Data can be accessed at https://www.iitp.ac.in/ ai-nlp-ml/resources.html#MHLCD or https://github.com/Mishrakshitij/Po-Em-MHLCDS

The World Health Organization (WHO) has significantly emphasized the need for mental health care.The social stigma associated with mental illness prevents individuals from addressing their issues and getting assistance.In such a scenario, the relevance of online counseling has increased dramatically.The feelings and attitudes that a client and a counselor express towards each other result in a higher or lower counseling experience.A counselor should be friendly and gain clients' trust to make them share their problems comfortably.Thus, it is essential for the counselor to adequately comprehend the client's emotions and ensure client's welfare, i.e. s/he should adapt and deal with the clients politely and empathetically to provide a pleasant, cordial and personalized experience.Motivated by this, in this work, we attempt to build a novel Polite and empAthetic counseLing conversational agent PAL.To have client's emotion-based polite and empathetic responses, two counseling datasets laying down the counseling support to substance addicts and crime victims are annotated.These annotated datasets are used to build PAL in a reinforcement learning framework.A novel reward function is formulated to ensure correct politeness and empathy preferences as per client's emotions with naturalness and non-repetitiveness in responses.Thorough automatic and human evaluation showcases the usefulness and strength of the designed novel reward function.Our proposed system is scalable and can be easily modified with different modules of preference models as per need 1 .

Although there has been a plethora of work on open-domain conversational systems, most of these lack the mechanism of controlling the concept transitions in a dialogue.For activities like switching from casual chit-chat to taskoriented conversation, an agent with the ability to manage the flow of concepts in a conversation might be helpful.The user would find the dialogue more fascinating and engaging and be more receptive to such transitions if these concept transitions were made while taking into account the user's persona.Focusing on personaaware concept transitions, we propose a Reinforced Persona-aware Topic-guiding Conversational System (RPTCS).Due to the lack of a persona-aware topic transition dataset, we propose a novel conversation dataset creation mechanism in which the conversational agent leads the discourse to drift to a set of target concepts depending on the persona of the speaker and the context of the conversation.To avoid scarcely available expensive human resources, the entire data-creation process is mostly automatic with human-in-loop only for quality checks.This created conversational dataset named PTCD is used to develop the RPTCS in two steps.First, a maximum likelihood estimation loss-based dialogue model is trained on PTCD.The trained model is then fine-tuned in a Reinforcement Learning (RL) framework by employing novel reward functions to assure persona, topic, and context consistency with non-repetitiveness in generated responses.Our experimental results demonstrate the strength of the proposed system with respect to strong baselines 1 .

The shortage of therapists for mental health patients emphasizes the importance of globally accessible dialogue systems alleviating their issues.To have effective interpersonal psychotherapy, these systems must exhibit politeness and empathy when needed.However, these factors may vary as per the user's gender, age, persona, and sentiment.Hence, in order to establish trust and provide a personalized cordial experience, it is essential that generated responses should be tailored to individual profiles and attributes.Focusing on this objective, we propose e-THERAPIST, a novel polite interpersonal psychotherapy dialogue system to address issues like depression, anxiety, schizophrenia, etc.We begin by curating a unique conversational dataset for psychotherapy, called PSYCON.It is annotated at two levels: (i) dialogue-level -including user's profile information (gender, age, persona) and therapist's psychotherapeutic approach; and (ii) utterance-level -encompassing user's sentiment and therapist's politeness, and interpersonal behaviour.Then, we devise a novel reward model to adapt correct polite interpersonal behaviour and use it to train e-THERAPIST on PSYCON employing NLPO loss.Our extensive empirical analysis validates the effectiveness of each component of the proposed e-THERAPIST demonstrating its potential impact in psychotherapy settings 1 .

The World Health Organization has underlined the significance of expediting the preventive measures for crime against women and children to attain the United Nations Sustainable Development Goals 2030 (promoting well-being, gender equality, and equal access to justice). The crime victims typically need mental health and legal counselling support for their ultimate well-being and sometimes they need to be persuaded to seek desired support. Further, counselling interactions should adopt correct politeness and empathy strategies so that a warm, amicable, and respectful environment can be built to better understand the victims’ situations. To this end, we propose PARTNER, a Politeness and empAthy strategies-adaptive peRsuasive dialogue sysTem for meNtal health and LEgal counselling of cRime victims. For this, first, we create a novel mental HEalth and legAl counseLling conversational dataset HEAL, annotated with three distinct aspects, viz. counselling act, politeness strategy, and empathy strategy. Then, by formulating a novel reward function, we train a counselling dialogue system in a reinforcement learning setting to ensure correct counselling act, politeness strategy, and empathy strategy in the generated responses. Extensive empirical analysis and experimental results show that the proposed reward function ensures persuasive counselling responses with correct polite and empathetic tone in the generated responses. Further, PARTNER proves its efficacy to engage the victim by generating diverse and natural responses.

Politeness is an expression in language that eases the conversation toward a positive undertone. If there is a display of rudeness, even the finest communication can fall through. In addition, if lathered with politeness, even the most angst-prone scenario can be expressed with far less hurt. In this article, we address the task of identifying politeness in goal-oriented dialog systems. In this regard, we create politeness-annotated conversational data (PACD) utilizing Microsoft Dialogue Challenge and DSTC1 datasets. For correctly identifying the politeness, we employ a hierarchical transformer network that effectively captures the contextual information (i.e., previous utterances) and current input for predicting the politeness in a given utterance of a dialog. Empirical results demonstrate that our proposed approach outperforms all the defined baselines. Furthermore, through in- and cross-domain experiments, we show the necessity of a PACD to mitigate acts such as rude requests or insults for both socially interactive and task-oriented dialog systems.

Persuasive conversations for a social cause often require influencing other person’s attitude or intention that may fail even with compelling arguments. The use of emotions and different types of polite tones as needed with facts may enhance the persuasiveness of a message. To incorporate these two aspects, we propose a polite, empathetic persuasive dialogue system (PEPDS). First, in a Reinforcement Learning setting, a Maximum Likelihood Estimation loss based model is fine-tuned by designing an efficient reward function consisting of five different sub rewards viz. Persuasion, Emotion, Politeness-Strategy Consistency, Dialogue-Coherence and Non-repetitiveness. Then, to generate empathetic utterances for non-empathetic ones, an Empathetic transfer model is built upon the RL fine-tuned model. Due to the unavailability of an appropriate dataset, by utilizing the PERSUASIONFORGOOD dataset, we create two datasets, viz. EPP4G and ETP4G. EPP4G is used to train three transformer-based classification models as per persuasiveness, emotion and politeness strategy to achieve respective reward feedbacks. The ETP4G dataset is used to train an empathetic transfer model. Our experimental results demonstrate that PEPDS increases the rate of persuasive responses with emotion and politeness acknowledgement compared to the current state-of-the-art dialogue models, while also enhancing the dialogue’s engagement and maintaining the linguistic quality.

Electroencephalogram signal is the signal used for the detection of a neurological disorder as epilepsy disorder, sleep disorder and many more. The types of EEG signal gives the hidden information regarding the distribution of the data that may consist of a large volume of the poor and noisy signal. In order to reduce the outlier effects and noise, incorporation of prior knowledge in the model, universum may help and enhance the better generalization ability of the model. This paper proposes a regularized universum twin support vector machine (RUTWSVM) for classification of the healthy and seizure EEG signals. Here, the selection of the universum data points is obtained in two ways (i). Universum data has been generated from the healthy and seizure EEG signals itself and (ii). Interictal EEG signal has been used as universum data which may help to handle the outlier effects. Further, various feature selection techniques are applied to extract the important noise free features from the EEG signals. We have performed a comparative analysis of proposed RUTWSVM with USVM and UTWSVM to classify the EEG signals as well as benchmark real-world datasets in an optimum way. The experiment results clearly exhibit the applicability and usability of the proposed RUTWSVM with interictal EEG signals as universum data points as well as benchmark real-world datasets.