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Modeling Multiple Support Strategies within a Single Turn for Emotional Support Conversations

Jie Zhu, Huaixia Dou, Junhui Li, Lifan Guo, Feng Chen, Jinsong Su, Chi Zhang, Fang Kong

arXiv:2604.17972v1cs.CL

TL;DR

Existing emotional support conversation studies largely assume one strategy per turn, despite supportive utterances often combining multiple strategies. This paper proposes two multi-strategy generation methods with cognitive reasoning and reinforcement learning, and evaluations on ESConv find multi-strategy modeling feasible and beneficial.

  • Problem

    Prior ESC studies largely restrict each turn to one strategy-response pair, leaving the benefits of multiple strategies within a single utterance unclear.

  • Method

    The paper proposes All-in-One and One-by-One generation methods, enhanced with cognitive reasoning and reinforcement learning, for producing utterances with multiple strategy-response pairs.

  • Results

    Utterance- and dialogue-level evaluations on ESConv show that the proposed methods model multi-strategy utterances effectively and outperform the Single-Strategy baseline.

  • Takeaways & Limitations

    The findings support flexible multi-strategy modeling as a feasible and beneficial approach for more natural and effective emotional support systems.

  • Takeaways & Limitations

    Models generate fewer multi-strategy utterances than ESConv, dialogue-level evaluation uses simulated seekers, and the study tests only ESConv.

Abstract

from arXiv · show

Emotional Support Conversation (ESC) aims to assist individuals experiencing distress by generating empathetic and supportive dialogue. While prior work typically assumes that each supporter turn corresponds to a single strategy, real-world supportive communication often involves multiple strategies within a single utterance. In this paper, we revisit the ESC task by formulating it as multi-strategy utterance generation, where each utterance may contain one or more strategy-response pairs. We propose two generation methods: All-in-One, which predicts all strategy-response pairs in a single decoding step, and One-by-One, which iteratively generates strategy-response pairs until completion. Both methods are further enhanced with cognitive reasoning guided by reinforcement learning to improve strategy selection and response composition. We evaluate our models on the ESConv dataset under both utterance-level and dialogue-level settings. Experimental results show that our methods effectively model multi-strategy utterances and lead to improved supportive quality and dialogue success. To our knowledge, this work provides the first systematic empirical evidence that allowing multiple support strategies within a single utterance is both feasible and beneficial for emotional support conversations. All code and data will be publicly available at https://github.com/aliyun/qwen-dianjin.

1 Introduction

The paper reformulates emotional support conversation as multi-strategy utterance generation because a single supportive utterance can contain multiple strategy-response pairs. It proposes two generation methods and evaluates them at utterance and dialogue levels.

  • Prior ESC work typically models each supporter turn as one strategy-response pair, overlooking naturally multi-strategy supportive utterances.
  • The paper defines an utterance as a supporter’s full turn and a response as the segment expressing one specific support strategy.
  • All-in-One generates all strategy-response pairs in one decoding step, whereas One-by-One iteratively generates pairs until stopping or reaching a predefined limit.
  • Both methods add cognitive reasoning and reinforcement learning to improve strategy selection and supportive response quality.
  • Utterance-level evaluation tests next-response generation against references, while dialogue-level evaluation measures support maintenance, strategy adaptation, and progress toward emotional relief.
  • Comprehensive utterance- and dialogue-level experiments show that multi-strategy utterance generation is feasible and beneficial for emotional support systems.

2 Background

ESConv provides the dataset basis for reformulating emotional support conversation around utterances containing multiple strategy-response pairs. The formulation targets the complete supportive utterance rather than a single strategy-linked response.

  • ESConv contains 1,300 emotional support dialogues annotated with eight support-strategy types.
  • The dataset is split into 1,040 training, 130 validation, and 130 testing conversations.
  • 2.1 Multi-Strategy Utterances in ESConv: 17.7% of 15,325 supporter utterances use at least two strategies within one turn, motivating multi-strategy task formulation.
  • 2.2 Problem Formulation: Each target supportive utterance is decomposed into response segments, with each segment associated with a specific support strategy.
  • 2.2 Problem Formulation: Unlike prior single-strategy prediction, the proposed task generates the entire supportive utterance containing one or more strategy-aligned responses.

3 Methodology

The paper introduces two ways to generate multiple strategy-response pairs within one supportive utterance: All-in-One decodes them jointly, while One-by-One generates them iteratively. Both methods incorporate structured cognitive reasoning, reinforcement learning, and task-specific rewards to improve strategy selection, response generation, and termination.

  • All-in-One Method: All-in-One generates the complete strategy-response sequence in a single decoding process, placing each strategy immediately before its associated response.The sequence is trained autoregressively to maximize the likelihood of the target output.
  • One-by-One Method: One-by-One generates one strategy-response pair and a termination indicator per step until completion or a predefined maximum number of steps.Inference uses K = 3 because nearly all validation utterances contain at most three strategies.
  • Cognitive Reasoning: Both methods augment each utterance with a four-node reasoning chain covering Context, Cognition, Emotion, and Support Plan.Reasoning annotations are distilled from four larger language models to capture diverse reasoning styles and complementary strengths.
  • Reinforcement Learning: GRPO uses structural-validity and strategy-matching rewards, while One-by-One additionally rewards correct stop-flag prediction.The final One-by-One reward encourages both accurate strategy prediction and appropriate termination behavior.
  • Reinforcement Learning: The All-in-One strategy reward uses the Levenshtein Ratio to compare predicted and reference strategy sequences.Single-strategy instances are downsampled because multi-strategy instances are less frequent, balancing the reward distribution.

4 Experimentation

Experiments on ESConv evaluate multi-strategy generation at utterance and dialogue levels, including strategy prediction, response quality, strategy composition, and self-play success. Results show that reasoning-enhanced All-in-One and One-by-One methods improve multi-strategy modeling and supportive dialogue outcomes, with sequential generation generally strongest.

  • Experimental settings: The study evaluates ESConv models using utterance-level metrics, dialogue-level self-play, strategy-count analysis, reasoning-distillation comparisons, and human judgments.Utterance evaluation includes EMR, LR, ADL, BLEU, ROUGE, and BERTScore; dialogue evaluation considers success rate, average turns, and average strategies.
  • Reasoning and reinforcement learning: Cognitive reasoning improves performance across All-in-One and One-by-One metrics, while reinforcement learning improves most metrics but can slightly lower One-by-One BLEU.For single-strategy utterances, reasoning and reinforcement learning substantially improve generation; for multi-strategy utterances, both consistently improve EMR and quality metrics.
  • Multi-strategy generation: 18.9% of reference utterances contain multiple strategies, compared with 2.7% and 1.1% in base All-in-One and One-by-One outputs; reasoning and reinforcement learning raise these to 8.4% and 7.7%.The authors attribute the initial gap to training-data dominance by single-strategy instances.
  • Dialogue-level evaluation: Both multi-strategy methods achieve higher dialogue success rates than the Single-Strategy baseline with comparable average turns and strategy counts.Instruction-only LLMs obtain zero success within ten turns, while human annotators rank both multi-strategy methods higher across all evaluated dimensions.

5 Related Work

Prior ESC research largely models each supporter turn with one strategy, despite supportive utterances often combining multiple strategies. This paper addresses the resulting underexplored question of whether multi-strategy generation is beneficial.

  • ESConv became a widely used ESC benchmark, and most prior work treats each supporter turn as associated with a single strategy.
  • Existing approaches use commonsense knowledge and multi-turn strategy modeling, but generally retain the one-strategy-per-turn formulation.
  • Nearly all existing studies leave multiple strategies within one utterance underexplored, and whether this is beneficial remains unclear.

6 Conclusion

The paper studies supportive utterances containing one or more strategy-response pairs and introduces two cognitively enhanced generation methods. Evaluations on ESConv show that multi-strategy modeling is feasible, beneficial, and consistently outperforms the Single-Strategy baseline.

  • The All-in-One and One-by-One methods generate complete supportive utterances either in one pass or through iterative strategy-response prediction.
  • Both methods incorporate cognitive reasoning and reinforcement learning to support multi-strategy utterance generation.
  • Extensive utterance-level and dialogue-level evaluations on ESConv show that multi-strategy modeling is feasible and beneficial.
  • The proposed methods consistently outperform the Single-Strategy baseline and support more natural and effective emotional support systems.

Limitations

The study identifies limitations in generated strategy diversity, dialogue-level evaluation realism, and dataset coverage.

  • The models generate fewer multi-strategy utterances than the ESConv dataset, potentially limiting response diversity and richness.
  • Dialogue-level evaluation uses simulated seekers, which may not fully capture real human emotional interactions.
  • Because the study focuses on ESConv, evaluation on other datasets or real-world deployment is needed to assess generalizability.

Ethical Considerations

The paper frames ESC deployment as requiring safeguards because the system is not validated for clinical use and may generate harmful or misleading content. It uses structured reasoning and ESConv data, but these measures do not guarantee safety.

  • The system is intended for everyday supportive communication, not as a replacement for professional psychological or medical assistance.
  • Deployment in high-risk or crisis situations requires safeguards against misuse.
  • The model may generate inappropriate, biased, or misleading content, and reasoning mechanisms plus reinforcement learning do not guarantee safety.
  • Real-world applications require additional content moderation and human oversight because training data may transmit biases.
  • Experiments use only the open-access ESConv dataset, which contains no personally identifiable or sensitive user information, with no additional user data collected.
  • The proposed cognitive reasoning chain models context, cognition, emotion, and a support plan to guide strategy selection and response generation.

B Training Details

The appendix reports data collection and training details for supervised fine-tuning and reinforcement learning, including implementation settings and approximate API-related costs.

  • Table 8 reports the numbers of instances collected for supervised fine-tuning and reinforcement learning.
  • Supervised fine-tuning uses LLaMA-Factory with LoRA, four NVIDIA A100 80GB GPUs, a learning rate of 3e-5, and five training epochs.LoRA settings differ between the All-in-One and One-by-One configurations.
  • Data distillation, self-play evaluation, and other advanced-model API calls cost approximately 800 USD in total.
  • The appendix identifies an utterance-level evaluation table for instruction-following LLMs using the All-in-One method.

D Case Study

The case study compares single-strategy and multi-strategy systems in controlled emotional-support scenarios, emphasizing how reasoning and strategy organization shape responses and dialogue progress.

  • Controlled comparisons hold the conversational context constant to make supportive quality, reasoning depth, and strategy integration more apparent.
  • DeepSeek-R1 applies two supportive strategies but lacks visible deliberate reasoning, limiting response depth and leaving no clear course of action.
  • The Single-Strategy baseline uses only advice-giving, producing a surface-level directive without explicit reflection on the seeker’s emotional state.
  • All-in-One with reasoning and reinforcement learning combines node-based situational and emotional analysis into a cohesive blend of empathy and solution.
  • One-by-One with reasoning and reinforcement learning separates emotional alignment from problem-solving, potentially improving receptivity and trust.
  • In dialogue examples, both enhanced methods provide comparable or greater strategic diversity within fewer turns.

F Guideline of Human Evaluation

The human-evaluation guideline assesses emotional-support performance across complete conversations with the same seeker, using standardized roles, scenarios, and evaluation objectives.

  • The guideline uses a scenario involving anxiety and deteriorating friendships during the COVID-19 pandemic.
  • The supporter prompt assigns responsibility for empathy, emotional connection, comfort, and deciding whether to continue responding or pause.
  • The reasoning prompt asks the psychological support assistant to explain decisions using dialogue context, the final reply, and strategy tags in execution order.
  • Self-play prompts separately define seeker simulation and critic assessment of whether the patient’s emotional issue has been solved.
  • The appendix includes prompts for profile extraction, seeker simulation, and human evaluation guidelines adapted from prior work.
  • The evaluation objective is to assess three supporters across entire conversations with the same seeker while maintaining objective and neutral judgments.
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