Papers by Priyanshu Priya

9 papers
On the Way to Gentle AI Counselor: Politeness Cause Elicitation and Intensity Tagging in Code-mixed Hinglish Conversations for Social Good (2024.findings-naacl)

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Challenge: Politeness is a multifaceted concept influenced by individual perceptions of what is considered polite or impolite.
Approach: They propose a task to identify the underlying reasons behind the use of politeness and gauge the degree of politity conveyed.
Outcome: The proposed method is compared against state-of-the-art datasets and their results show it is superior.
EmoInHindi: A Multi-label Emotion and Intensity Annotated Dataset in Hindi for Emotion Recognition in Dialogues (2022.lrec-1)

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Challenge: Existing datasets for emotion recognition in dialogues are in English . existing datasets are limited to a few languages like Hindi .
Approach: They propose a large conversational dataset in Hindi for multi-label emotion and intensity recognition in conversations . they use a Wizard-of-Oz manner to annotate dialogues with 16 emotion labels .
Outcome: The proposed dataset contains 1,814 dialogues with 44,247 utterances in Hindi . it is based on a Wizard-of-Oz manner and can detect emotions in conversation .
MENDER: Multi-hop Commonsense and Domain-specific CoT Reasoning for Knowledge-grounded Empathetic Counseling of Crime Victims (2025.naacl-srw)

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Challenge: Experimental evaluations on counseling dialogue dataset, POEM validate MENDER’s efficacy in generating coherent, knowledge-grounded responses.
Approach: They propose a multi-hop commonsensE and domaiN-specific Chain-of-Thought reasoning framework that integrates commonsense and domain knowledge via multi-hopping reasoning over the dialogue context.
Outcome: Experimental evaluations on counseling dialogue dataset validate MENDER’s efficacy in generating coherent, empathetic, knowledge-grounded responses.
e-THERAPIST: I suggest you to cultivate a mindset of positivity and nurture uplifting thoughts (2023.emnlp-main)

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Challenge: Existing mental health workforce is struggling to meet the needs adequately.
Approach: They propose a novel polite interpersonal psychotherapy dialogue system that is annotated at two levels: dialogue-level and utterance-level.
Outcome: The proposed system can address depression, anxiety, schizophrenia and other mental health issues.
PAL to Lend a Helping Hand: Towards Building an Emotion Adaptive Polite and Empathetic Counseling Conversational Agent (2023.acl-long)

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Challenge: The social stigma associated with mental illness prevents individuals from addressing their issues and getting assistance.
Approach: They propose to build a Polite and empAthetic conversational agent PAL to lay down the counseling support to substance addicts and crime victims.
Outcome: The proposed agent is scalable and can be easily modified with different modules of preference models as per need.
We Argue to Agree: Towards Personality-Driven Argumentation-Based Negotiation Dialogue Systems for Tourism (2025.findings-emnlp)

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Challenge: Argumentation mechanisms are integrated into negotiation dialogue systems to improve conflict resolution and adaptability.
Approach: They propose a dataset of Argumentation Profile, Preference Profile, and Buying Style Profiles to generate personality-driven dialogues in negotiation dialogue systems.
Outcome: The proposed task improves argumentation mechanisms and adaptability by aligning interactions with individuals’ preferences and styles.
TRIP NEGOTIATOR: A Travel Persona-aware Reinforced Dialogue Generation Model for Personalized Integrative Negotiation in Tourism (2024.findings-emnlp)

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Challenge: Incorporating traveler preferences, constraints, and expectations allows for customizing negotiation strategies, resulting in a more personalized and integrative experience.
Approach: They propose a novel travel persona-aware Reinforced dIalogue generation model for personalized integrative negotiation in the tourism domain.
Outcome: The proposed system generates coherent and diverse responses consistent with the traveler's personality.
PRISMA: Preference-Reinforced Self-Training Approach for Interpretable Emotionally Intelligent Negotiation Dialogues (2026.acl-long)

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Challenge: Emotion plays a pivotal role in shaping negotiation outcomes, influencing trust, cooperation, and long-term relationships.
Approach: They propose an Emotion-aware Negotiation Strategy-informed Chain-of-Thought reasoning mechanism which mimics human negotiation by perceiving, understanding, using, and managing emotions.
Outcome: The proposed system generates interpretable emotions and improves negotiation effectiveness on job interviews and resource allocation datasets.
Knowledge-enhanced Response Generation in Dialogue Systems: Current Advancements and Emerging Horizons (2024.lrec-tutorials)

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Challenge: Knowledge-enhanced Dialogue Systems (KEDS) are a new approach to enhancing human-machine interaction through natural language.
Approach: This tutorial provides an in-depth exploration of Knowledge-enhanced Dialogue Systems (KEDS) it aims to elucidate their significance, highlight advances made using deep learning, and pinpoint the current challenges.
Outcome: The tutorial aims to give attendees a comprehensive understanding of KEDS, and highlight advances made using deep learning and pinpoint the current challenges.

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