E mo D ynami X : Emotional Support Dialogue Strategy Prediction by Modelling M i X ed Emotions and Discourse Dynamics (2025)

Abstract: Designing emotionally intelligent conversational systems to provide comfort and advice to people experiencing distress is a compelling area of research. Recently, with advancements in large language models (LLMs), end-to-end dialogue agents without explicit strategy prediction steps have become prevalent. However, implicit strategy planning lacks transparency, and recent studies show that LLMs’ inherent preference bias towards certain socio-emotional strategies hinders the delivery of high-quality emotional support. To address this challenge, we propose decoupling strategy prediction from language generation, and introduce a novel dialogue strategy prediction framework, EmoDynamiX, which models the discourse dynamics between user fine-grained emotions and system strategies using a heterogeneous graph for better performance and transparency. Experimental results on two ESC datasets show EmoDynamiX outperforms previous state-of-the-art methods with a significant margin (better proficiency and lower preference bias). Our approach also exhibits better transparency by allowing backtracing of decision making.

## introduction providing early intervention for individuals experiencing distress from life challenges is crucial for enabling them to transition toward positive lifestyles and, consequently, fostering a more caring society. this need has inspired the nlp community to develop effective emotional support conversation (esc) systems @xcite . these systems aim to alleviate the distress of help-seekers and can be seen as a first step in helping them to find healthcare professionals. recently, with the release of multi-turn and human-evaluated esc datasets ( @xcite , data-driven approaches have begun to surpass rule-based methods @xcite . previous work on data-driven esc has primarily focused on modular dialogue systems as defined by @xcite . modular systems feature a three-fold workflow: recognizing, planning, and generating. examples include @xcite , @xcite , @xcite and @xcite . in these systems, the socio-emotional strategy is selected based on the recognition of the user's state, and responses are generated using customized language decoders conditioned on the predicted strategy. with the emergence of large language models (llms) offering enhanced capabilities, llms have increasingly dominated both modular and, particularly, end-to-end esc systems @xcite , where strategy planning has shifted from an explicit process to a more implicit, hidden mechanism. however, implicit dialogue strategy planning with llms faces two challenges. first, transparency is often lacking in such implicit decisionmaking processes due to the well-known "blackbox" property of llms @xcite @xcite . second, recent studies show that preference biases inherited from pretraining data often cause llms to struggle with balancing social-oriented and task-oriented goals. @xcite found that in peer-tutoring dialogues, llms like chatgpt frequently prioritize non-hedging strategies, even in situations where hedging strategies would be more appropriate for repairing low rapport between peers. similarly, @xcite observed that llms' strong predisposition towards certain strategies can undermine the outcome of the current stage of esc. as a result, the overall objectives may be significantly compromised. to address this limitation, introducing external strategy planners, which offer greater controllability by enabling us to explicitly exclude inappropriate strategies in specific contexts, stands out to be a promising solution. it has been evidenced by both automatic metrics and human evaluations that, an explicit decision-making module can more effectively mitigate preference bias, and improved proficiency in dialogue strategy actually enhances overall generation quality @xcite . this foundational insight leads us to isolate and focus specifically on dialogue strategy prediction-a previously intermediate step-with three primary goals: (1) better alignment with human expert strategies, (2) reduced preference bias, and (3) improved transparency. additionally, as more powerful llms and improved controlled generation techniques continue to emerge @xcite , focusing on dialogue strategy prediction offers a more economical pathway to addressing current dialogue system limitations. explicit dialogue strategy prediction can serve as a flexible, plug-and-play module compatible with state-of-the-art llms or as a foundational component for future rl-based methods, where better alignment with human expert strategies is typically a critical first step @xcite . therefore, we treat socio-emotional strategy prediction as an independent task, as previously explored by @xcite . we illustrate the scope of this task in figure 1 . from this perspective, we raise the following research questions: in addressing rq1, we introduce emodynamix, a decision-making framework that integrates multiple expert models and incorporates a heterogeneous graph learning module to capture the dynamic interactions between system strategies and user emotions. with graphs, we backtrace the decisionmaking process, making a step towards greater transparency. we also utilize dummy nodes @xcite for role-aware information aggregation, enhancing the overall performance. for rq2, we design a mixed-emotion module to effectively integrate erc into our framework: we validate the effectiveness of our proposed framework through comparative experiments on two public esc datasets. the results demonstrate that emodynamix significantly outperforms all previous baselines, achieving superior f1 scores and a notable reduction in preference bias. ## problem formulation the task of predicting the next dialogue strategy can be written as a multi-class classification problem: assuming a dialogue comprising t speaker turns, we define the dialogue history as h @xmath0, a t , st t } where u @xmath1, and each u @xmath2. a @xmath3, with a @xmath4, system}. st @xmath5, but they only exist for the agent. noting s the set of strategies, i @xmath6, a @xmath7, and denoting similarly i agent , we have that ∀@xmath8, st @xmath9, st @xmath10. our task is, given a fixed window size of n -1, to predict the strategy for agent speaker turns, which we formulate as estimating the probability distribution p(st n | h n -1 1 ) upon s when @xmath11. for the sake of simplicity, from now on we will use indexing from the beginning of the context window and no longer from the entire conversation. ## methodology our framework (see figure 2 ) comprises three main components: (1) a semantic modelling module for capturing the semantics of the dialogue context; (2) a heterogeneous graph learning module, designed to capture the complex interplay between the user's emotions and system strategies within the dialogue history; and (3) an mlp classification head which integrates the features obtained from the previous modules to produce the prediction result. ## in-depth analysis of emodynamix we illustrated a case study using a snippet from esconv, as shown in figure 3 . the case involves the agent deciding which strategy to apply after the user's emotional state has transitioned positively from frustration (as frustration is not a category in dailydialog, our mixed-emotion module models it as moderated sadness with a little anger) to joy. the ground truth here is affirmation and reassurance, which acknowledges the user's positive transition and encourages consolidation of positive mood. by looking at the attention weights of dummy node edges, we can observe the contribution of each node in decision-making. we notice that, as the rgat layer deepens, the dummy node shifts its attention from previously applied strategies to the user's emotional transition, with higher weights applied to edges connecting with the emotion state nodes (especially frustration). in short, our graph-learning module effectively captures clues from emotion/strategy dynamics. we further study the disagreements between the predictions and human strategies using the confusion matrix (appendix e.3). determining the appropriate timing for emotion-related dialogue strategies versus task-oriented ones is particularly challenging. the model frequently predicts providing suggestions whereas the human strategies are among the three emotion-related ones: reflec-tion of feelings, self-disclosure, and affirmation and reassurance. notably, over 60% of emotionrelated strategies were categorized as task-oriented ones, highlighting the difficulty in making choices between these two strategy categories, as also discussed by @xcite . we further analyze the disagreement patterns between predicted and human strategies. by looking at their correlations with the primary emotion categories of emotion nodes with the highest attention weights (figure 4 ), we find that "neutral" contributes a larger proportion to these disagreements in general compared to its overall representation in the erc module's output distribution (59.22%, as shown in appendix e.2). this suggests that strategies are easier to predict when the model can pick up the user emotions expressed in the context. ## conclusions in this paper, we propose emodynamix, a socioemotional dialogue strategy prediction framework that aggregates expert models and uses heterogeneous graphs to model the conversational dynamics of user states and system strategies. our approach significantly improves all baselines on two public esc datasets, and takes a step towards transparency by analyzing attention weights in the indepth study.