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1. ResPer: Computationally Modelling Resisting Strategies in Persuasive Conversations

   Ritam Dutt, Sayan Sinha (July 2021, Proceedings of the European Chapter of the Association for Computational Linguistics)

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   Modelling persuasion strategies as predictors of task outcome has several real-world applications and has received considerable attention from the computational linguistics community. However, previous research has failed to account for the resisting strategies employed by an individual to foil such persuasion attempts. Grounded in prior literature in cognitive and social psychology, we propose a generalised framework for identifying resisting strategies in persuasive conversations. We instantiate our framework on two distinct datasets comprising persuasion and negotiation conversations. We also leverage a hierarchical sequence-labelling neural architecture to infer the aforementioned resisting strategies automatically. Our experiments reveal the asymmetry of power roles in non-collaborative goal-directed conversations and the benefits accrued from incorporating resisting strategies on the final conversation outcome. We also investigate the role of different resisting strategies on the conversation outcome and glean insights that corroborate with past findings. We also make the code and the dataset of this work publicly available at this https URL. 

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2. Pipeline for Cultural Context Grounding of Conversations

   Pujari, Rajkumar; Goldwasser, Dan (May 2025, Association for Computational Linguistics)

   Conversations often adhere to well-understood social norms that vary across cultures. For example, while addressing work superiors by their first name is commonplace in the Western culture, it is rare in Asian cultures. Adherence or violation of such norms often dictates the tenor of conversations. Humans are able to navigate social situations requiring cultural awareness quite adeptly. However, it is a hard task for NLP models. In this paper, we tackle this problem by introducing a Cultural Context Schema for conversations. It comprises (1) conversational information such as emotions, dialogue acts, etc., and (2) cultural information such as social norms, violations, etc. We generate ∼110k social norm and violation descriptions for ∼23k conversations from Chinese culture using LLMs. We refine them using automated verification strategies which are evaluated against culturally aware human judgements. We organize these descriptions into meaningful structures we call Norm Concepts, using an interactive human-in-the-loop framework. We ground the norm concepts and the descriptions in conversations using symbolic annotation. Finally, we use the obtained dataset for downstream tasks such as emotion, sentiment, and dialogue act detection. We show that it significantly improves the empirical performance. 

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3. Finding Your Voice: The Linguistic Development of Mental Health Counselors

   Zhang, Justine; Filbin, Robert; Morrison, Christine; Weiser, Jaclyn; Danescu-Niculescu-Mizil, Cristian (January 2019, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics)

   Mental health counseling is an enterprise with profound societal importance where conversations play a primary role. In order to acquire the conversational skills needed to face a challenging range of situations, mental health counselors must rely on training and on continued experience with actual clients. However, in the absence of large scale longitudinal studies, the nature and significance of this developmental process remain unclear. For example, prior literature suggests that experience might not translate into consequential changes in counselor behavior. This has led some to even argue that counseling is a profession without expertise. In this work, we develop a computational framework to quantify the extent to which individuals change their linguistic behavior with experience and to study the nature of this evolution. We use our framework to conduct a large longitudinal study of mental health counseling conversations, tracking over 3,400 counselors across their tenure. We reveal that overall, counselors do indeed change their conversational behavior to become more diverse across interactions, developing an individual voice that distinguishes them from other counselors. Furthermore, a finer-grained investigation shows that the rate and nature of this diversification vary across functionally different conversational components. 

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4. ConvoKit: A Toolkit for the Analysis of Conversations

   Chang, Jonathan P; Chiam, Caleb; Fu, Liye; Wang, Andrew; Zhang, Justine; Danescu-Niculescu-Mizil, Cristian (July 2020, Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue)

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   This paper describes the design and functionality of ConvoKit, an open-source toolkit for analyzing conversations and the social interactions embedded within. ConvoKit provides an unified framework for representing and manipulating conversational data, as well as a large and diverse collection of conversational datasets. By providing an intuitive interface for exploring and interacting with conversational data, this toolkit lowers the technical barriers for the broad adoption of computational methods for conversational analysis. 

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5. Dialo-AP: A Dependency Parsing Based Argument Parser for Dialogues

   Sougata Saha; Souvik Das; Rohini K. Srihari (October 2022, Proceedings of the 29th International Conference on Computational Linguistics (COLING))

   While neural approaches to argument mining (AM) have advanced considerably, most of the recent work has been limited to parsing monologues. With an urgent interest in the use of conversational agents for broader societal applications, there is a need to advance the state-of-the-art in argument parsers for dialogues. This enables progress towards more purposeful conversations involving persuasion, debate and deliberation. This paper discusses Dialo-AP, an end-to-end argument parser that constructs argument graphs from dialogues. We formulate AM as dependency parsing of elementary and argumentative discourse units; the system is trained using extensive pre-training and curriculum learning comprising nine diverse corpora. Dialo-AP is capable of generating argument graphs from dialogues by performing all sub-tasks of AM. Compared to existing state-of-the-art baselines, Dialo-AP achieves significant improvements across all tasks, which is further validated through rigorous human evaluation. 

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