In Psychotherapy, Early Maladaptive Schemas (EMS) are entrenched negative perceptions of self or others that perpetuate mental health challenges, contribute to treatment resistance and relapse, and obstruct therapeutic progress. Addressing EMS using appropriate psychotherapeutic support (PS) strategies helps resolve core emotional deficits, mitigate resistance, and improve client engagement. Moreover, adapting polite and empathetic communication based on clients’ emotional states fosters trust, emotional safety, and a conducive therapeutic environment, which is critical for addressing EMS and achieving positive outcomes. Motivated by these insights, we introduce MATE - a novel EMS-guided polite and empAthetic dialogue sysTem for psychothErapeutic support. MATE integrates a Large Language Model (LLM) with a Mixture of Experts-based Reinforcement Learning (MoE-RL) approach to overcome the limitations of traditional RL methods, such as large action spaces and generic responses. The LLM captures diverse semantic patterns from dialogue context. MoE-RL leverages dedicated psychotherapeutic, politeness, and empathy experts, along with a new reward function, comprising PS, politeness, empathy, contextual consistency, and diversity rewards to guide policy learning for effective response generation. Evaluations on the HOPE and PSYCON datasets demonstrate MATE’s efficacy in generating polite and empathetic psychotherapeutic responses based on clients’ EMS and emotional cues while ensuring contextual consistency and diversity.

Emotion plays a pivotal role in shaping negotiation outcomes, influencing trust, cooperation, and long-term relationships.Developing negotiation dialog systems that can recognize and respond strategically to emotions is, therefore, essential to create more effective human-centered interactions.Beyond generating emotionally appropriate responses, interpretability -understanding how a system generates a particular emotion-aware response, is critical for fostering reliability and building rapport.Driven by these aspects, in this work, we introduce PRISMA, an interpretable emotionally intelligent negotiation dialogue system targeting two application domains, viz.job interviews and resource allocation.To enable interpretability, we propose an Emotion-aware Negotiation Strategyinformed Chain-of-Thought (ENS-CoT) reasoning mechanism, which mimics human negotiation by perceiving, understanding, using, and managing emotions.Leveraging ENS-CoT, we curate two new datasets: JobNego (for job interview negotiation) and ResNego (for resource allocation negotiation).We then leverage these datasets to develop PRISMA by augmenting self-training with Direct Preference Optimization (DPO), guiding agents toward more accurate, interpretable, and emotionally appropriate negotiation responses.Automatic and human evaluation on JobNego and ResNego datasets demonstrate that PRISMA substantially enhances interpretability and generates appropriate emotion-aware responses, while improving overall negotiation effectiveness 1 .

Developing intelligent negotiation dialogue systems that resolve conflicts and promote equitable, inclusive, and sustainable outcomes is at the forefront of advancing automated negotiation technology for social good. Negotiation involves balancing cooperation and competition to maximize value without causing offense. Using polite language fosters mutual understanding and creates a respectful and collaborative environment essential for successful negotiations in various domains. Considering this, in this paper, we propose a polite negotiation dialogue system, GENTEEL-NEGOTIATOR for social good applications to boost the overall quality of negotiation outcomes. We focus on developing a negotiation dialogue system for two key application areas, namely tourism and e-commerce. We begin by curating a unique negotiation dialogue dataset, NEGOCHAT for tourism. We further enrich the NEGOCHAT and Integrative Negotiation Dataset (IND) for e-commerce with various negotiation strategies. These datasets are then used to develop the GENTEEL-NEGOTIATOR, leveraging the Large Language Model (LLM) and mixture-of-expert (MoE)-based reinforcement learning approach. The proposed MoE-based method employs heuristic experts dedicated to negotiation, politeness, and dialogue coherence to facilitate the learning of diverse semantics by analyzing the dialogue context. A novel reward function with negotiation strategy congruence, politeness, dialogue coherence, and engagingness rewards is designed to guide the policy’s learning for generating responses. Automatic and human evaluations on NEGOCHAT and IND datasets validate the effectiveness of GENTEEL-NEGOTIATOR in generating polite responses during negotiation while maintaining conversation goals, including coherence and engagingness.

Politeness is key to successful conversations. It depicts the behavior that is socially valued and is often accompanied by emotions. Previously, researchers have focused on detecting politeness in goal-oriented conversations in high-resource English language. The existing studies do not focus on identifying politeness in a resource-scared Indian languages such as Hindi, primarily due to the lack of labeled data. To overcome this limitation, in this article, we propose a novel emotion-aware contrastive learning (CL) method for zero-shot cross-lingual politeness identification (XeroPol) task in dialogues. We introduceContrastiveAligner, a CL-based alignment method for zero-shot cross-lingual transfer.ContrastiveAligneremploys translated data and pushes the model to generate similar utterance embeddings for different languages. As politeness and emotion are interrelated, hence, as the conversation progresses, the variation in emotions tends to pose challenges in identifying politeness in dialogues. Thus, in this work, we also design an auxiliary emotion-aware CL objective using sentiment information, namely theEmoSenti objective, which is expected to implicitly model the emotion change across utterances and help in the primary task of politeness identification. Experiments on MultiDoGo and EmoWOZ datasets demonstrate that the proposed approach significantly outperforms the baselines. Further analysis such as human evaluation on the EmoInHindi dataset validates the efficacy of the entire approach.

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 .

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.

The long-standing goal of Artificial Intelligence (AI) has been to create human-like conversational systems. Such systems should have the ability to develop an emotional connection with the users, consequently, emotion recognition in dialogues has gained popularity. Emotion detection in dialogues is a challenging task because humans usually convey multiple emotions with varying degrees of intensities in a single utterance. Moreover, emotion in an utterance of a dialogue may be dependent on previous utterances making the task more complex. Recently, emotion recognition in low-resource languages like Hindi has been in great demand. However, most of the existing datasets for multi-label emotion and intensity detection in conversations are in English. To this end, we propose a large conversational dataset in Hindi named EmoInHindi for multi-label emotion and intensity recognition in conversations containing 1,814 dialogues with a total of 44,247 utterances. We prepare our dataset in a Wizard-of-Oz manner for mental health and legal counselling of crime victims. Each utterance of dialogue is annotated with one or more emotion categories from 16 emotion labels including neutral and their corresponding intensity. We further propose strong contextual baselines that can detect the emotion(s) and corresponding emotional intensity of an utterance given the conversational context.