publications

2026

1. SIGDIAL

   A Reinforcement Learning-Based Facilitator for Simulated Group Motivational Interviewing

   Alafate Abulimiti, Vladislav Maraev, Agnès Helme-Guizon, and 1 more author

   In Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Aug 2026

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   Motivational Interviewing (MI) is a widely validated approach to behavior change, but existing virtual MI agents operate only in one-on-one settings, ignoring the cost-effectiveness and peer-support dynamics of group MI. We present a simulation environment and reinforcement learning (RL) based dialogue manager for group MI, in which a discrete Soft-Actor-Critic (SAC) policy selects therapist dialogue acts and a large language model generates utterances, with two LLM-prompted patient agents as interlocutors. Our model supports adaptation to different participant profiles. We compared our dialogue manager with four LLM-based ones at the dialogue acts level. We observed that RL yields a significantly different therapist policy, which showed the tendency to generate more directive acts and adapt to varying group compositions. Participant profile adaptation was the strongest in groups containing an open-to-change participant.

2025

1. SEMDIAL

   Social Repair

   Alafate Abulimiti and Vladislav Maraev

   In Proceedings of the 29th Workshop on the Semantics and Pragmatics of Dialogue – Full Papers, Sep 2025

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   Social repair encompasses the crucial strategies individuals employ to maintain and restore social relationships and interpersonal harmony when interactions go awry. While existing research on repair has often prioritized communicative and informational aspects, the social and relational dimensions have received less focused attention. This paper addresses this gap by developing a formal representation of social repair centered on the dynamic coordination of social beliefs between interlocutors. Drawing insights from social theories of interaction, Type Theory with Records within Dialogue Game Board theory, we define social repair and specify its key components, including an agent’s social beliefs. We then delineate the interactional dynamics through which social misalignments are perceived and addressed. These dynamics can lead to distinct reactive strategies by an interlocutor who recognizes a potential issue: accommodation to the other’s social belief, an attempt to calibrate the other’s interpretation, or ignoring the perceived misstep. This framework provides a systematic approach to understanding and modeling the nuanced processes of social repair, offering important implications for developing more socially aware agents.

2023

1. ACL

   How About Kind of Generating Hedges using End-to-End Neural Models?

   Alafate Abulimiti, Chloé Clavel, and Justine Cassell

   In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jul 2023

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   Hedging is a strategy for softening the impact of a statement in conversation. In reducing the strength of an expression, it may help to avoid embarrassment (more technically, “face threat”) to one’s listener. For this reason, it is often found in contexts of instruction, such as tutoring. In this work, we develop a model of hedge generation based on i) fine-tuning state-of-the-art language models trained on human-human tutoring data, followed by ii) reranking to select the candidate that best matches the expected hedging strategy within a candidate pool using a hedge classifier. We apply this method to a natural peer-tutoring corpus containing a significant number of disfluencies, repetitions, and repairs. The results show that generation in this noisy environment is feasible with reranking. By conducting an error analysis for both approaches, we reveal the challenges faced by systems attempting to accomplish both social and task-oriented goals in conversation.

2. SIGDIAL

   When to generate hedges in peer-tutoring interactions

   Alafate Abulimiti, Chloé Clavel, and Justine Cassell

   In Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Sep 2023

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   This paper explores the application of machine learning techniques to predict where hedging occurs in peer-tutoring interactions. The study uses a naturalistic face-to-face dataset annotated for natural language turns, conversational strategies, tutoring strategies, and nonverbal behaviors. These elements are processed into a vector representation of the previous turns, which serves as input to several machine learning models, including MLP and LSTM. The results show that embedding layers, capturing the semantic information of the previous turns, significantly improves the model’s performance. Additionally, the study provides insights into the importance of various features, such as interpersonal rapport and nonverbal behaviors, in predicting hedges by using Shapley values for feature explanation. We discover that the eye gaze of both the tutor and the tutee has a significant impact on hedge prediction. We further validate this observation through a follow-up ablation study.

2022

1. SEMDIAL

   Classifying the Response Space of Questions: A Machine Learning Approach

   Zulipiye Yusupujiang, Alafate Abulimiti, and Jonathan Ginzburg

   In Proceedings of the 26th Workshop on the Semantics and Pragmatics of Dialogue – Full Papers, Aug 2022

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2021

1. SEMDIAL

   “By the way, do you like Spider Man?”–Towards A Social Planning Model for Rapport

   Alafate Abulimiti, Justine Cassell, and Jonathan Ginzburg

   In Proceedings of the 25th Workshop on the Semantics and Pragmatics of Dialogue, 2021

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   Interaction takes place not only on the propositional level but also on the social level. In this paper, we consider rapport as an important social phenomenon in interaction. Moti- vated by data from the tutoring domain, we hypothesize that (i) off-task episodes are trig- gered by a low level of rapport and (ii) such episodes are means of raising the level of rapport. We sketch a planning model that allows off-task episodes to be triggered by (low) rap- port level, which we apply to two simple examples.

2020

1. SEMDIAL

   Different Games in Dialogue: Combining Character and Conversational Types in Strategic Choice

   Alafate Abulimiti

   In Proceedings of the 24th Workshop on the Semantics and Pragmatics of Dialogue – Full Papers, Jul 2020

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