ReflectDiffu: Reflect between Emotion-intent Contagion and Mimicry for Empathetic Response Generation via a RL-Diffusion Framework
Authors: Jiahao Yuan, Zixiang Di, Zhiqing Cui, Guisong Yang, Usman Naseem
Accepted at: acl25

ReflectDiffu: Reflect between Emotion-intent Contagion and Mimicry for Empathetic Response Generation via a RL-Diffusion Framework

Jiahao Yuan1,2*, Zixiang Di2, Zhiqing Cui3, Guisong Yang1†, Usman Naseem4
1University of Shanghai for Science and Technology
2East China Normal University
3Nanjing University of Information Science and Technology
4Macquarie University

Abstract
Empathetic response generation necessitates the integration of emotional and intentional dynamics to foster meaningful interactions. Existing research either neglects the intricate interplay between emotion and intent, leading to suboptimal controllability of empathy, or resorts to large language models (LLMs), which incur significant computational overhead. In this paper, we introduce ReflectDiffu, a lightweight and comprehensive framework for empathetic response generation. This framework incorporates emotion contagion to augment emotional expressiveness and employs an emotion-reasoning mask to pinpoint critical emotional elements. Additionally, it integrates intent mimicry within reinforcement learning for refinement during diffusion. By harnessing an intent twice reflect mechanism of Exploring-Sampling-Correcting, ReflectDiffu adeptly translates emotional decision-making into precise intent actions, thereby addressing empathetic response misalignments stemming from emotional misrecognition. Through reflection, the framework maps emotional states to intents, markedly enhancing both response empathy and flexibility. Comprehensive experiments reveal that ReflectDiffu outperforms existing models regarding relevance, controllability, and informativeness, achieving state-of-the-art results in both automatic and human evaluations.

1 Introduction
Empathetic dialogue generation endows dialogue models with human-like emotional capabilities to recognize, understand, and express emotions (Davis, 1990; Cuff et al., 2016). In psychology, empathy mechanisms are empirically linked to sociological studies on emotional contagion (Hatfield et al., 1993) and empathetic mimicry (Carr et al., 2003). Recent research has delved into various aspects of empathetic mechanisms in chatbots, including dynamically tailoring responses based on perceived emotional triggers (Gao et al., 2021, 2023) or mimicking empathetic emotions (Majumder et al., 2020; Bi et al., 2023).

Existing models typically generate responses based on either mimicking emotional states (Lin et al., 2019; Majumder et al., 2020) or incorporating external knowledge including multi-resolution strategies (Li et al., 2020), commonsense reasoning through predefined sources (Li et al., 2022a) or extracted via COMET (Hwang et al., 2021; Sabour et al., 2022), and multi-grained signals including causes (Bi et al., 2023; Hamad et al., 2024) to enhance contextual understanding.

Recent advances in large language models (LLMs) (Dubey et al., 2024; Yang et al., 2024a) have promoted several empathetic dialogue models utilizing multiple-stage Chain-of-Thought (CoT)

![An example from the EMPATHETICDIALOGUES dataset involves incorporating Emotional Contagion and Mimicking, using intent twice mechanism to enhance empathy.](page_1012_370_388_312.png)

Figure 1: An example from the EMPATHETICDIALOGUES dataset involves incorporating Emotional Contagion and Mimicking, using intent twice mechanism to enhance empathy.
(Chen et al., 2024; Hu et al., 2024) with fine-tuning (Zhang et al., 2020; Cai et al., 2024). However, their unstable performance (Lu et al., 2022; Xie et al., 2023; Yuan et al., 2024) and reliance on external knowledge and high training costs (Kaplan et al., 2020; Yuan et al., 2024) complicate practical implementation. Consequently, current research focuses on enhancing small-scale empathetic models through empathy mechanisms (Majumder et al., 2020; Gao et al., 2023; Zhou et al., 2023; Wang et al., 2024) as a more lightweight, practical alternative to LLMs. In summary, lightweight empathetic models encounter three major limitations: (1) They primarily rely on supplementary knowledge signals (Gao et al., 2023) rather than underlying psychological mechanisms, which impedes controllability and empathetic capability. (2) They often overlook the internal mechanisms behind emotional causes, emotions, and intents, which rely heavily on external knowledge or pre-trained annotators (Bi et al., 2023; Chen et al., 2024), resulting in hard-coded enhancements rather than genuine understanding and iterative correction, thus impacting empathy, diversity and flexibility. (3) There is a shortage of multi-task datasets for emotion reason masking, intent prediction, and empathetic dialogue. Most models rely on supplementary datasets for auxiliary tasks (Li et al., 2024; Bi et al., 2023), which does not guarantee that the advantages of multi-task training are fully realized or effectively aligned.

To address above limitations, we propose ReflectDiffu, a lightweight and comprehensive framework for empathetic response that seamlessly blends emotional contagion with intent prediction through a reflect mechanism. In sociology, empathetic actions are caused by emotional contagion (Hatfield et al., 1993) and empathetic mimicking (De Waal and Preston, 2017), which indicate an imitation feedback mechanism between human emotions and intentional actions (Rizzolatti and Craighero, 2005; Iacoboni, 2009), as depicted in Figure 1. Our key contributions include:

• We introduce a novel empathetic framework, ReflectDiffu, guided by sociological theories on emotional contagion and empathetic mimicry to improve empathy.
• We propose an intent twice mechanism, termed Exploring-Sampling-Correcting guided by reflect mechanism to align emotion with intent and minimize empathetic response misalignment caused by emotional misrecognition.
• We conducted extensive experiments demonstrating that ReflectDiffu outperforms state-of-the-art models in both automatic and human evaluations.

2 Related Work

2.1 Empathetic Response Generation

Empathetic response generation entails recognizing emotional states and producing suitable emotional responses (Davis, 1983; Rashkin et al., 2018). Early studies primarily aimed at generating emotion-specific responses based solely on emotional states (Lin et al., 2019; Majumder et al., 2020), but faced challenges with the explainability and controllability of empathy. Additionally, reinforcement learning (RL) has been employed to refine dialogue policies, with works like (Li et al., 2024) leveraging policy-based RL to optimize empathetic response generation. Recent studies have integrated external commonsense reasoning (Li et al., 2022a; Zhou et al., 2023), predefined knowledge (Li et al., 2020; Gao et al., 2023; Wang et al., 2024) or pre-trained causal factors (Hwang et al., 2021; Sabour et al., 2022) to enhance emotional perception, but they overlook the established interconnections among factors (De Waal and Preston, 2017), which restricts deeper interpretability and empathy.

Unlike previous approaches, ReflectDiffu introduces reflection interconnection to systematically convert emotional dimensions into actionable intents, thereby improving empathy.

2.2 Generative Model for Dialogue Generation

Generative models have exhibited outstanding performance, facilitating text generation. Majumder et al. (2020) pioneered introducing Variational Autoencoders (VAEs) (Park et al., 2018) to imbue text with empathetic expressions. Further research by Gao et al. (2023) introduced latent variables (Sohn et al., 2015) accounting for cognition, affection, and behavior to better model emotional dependencies in dialogues.

Subsequently, Denoising Diffusion Probabilistic Models (DDPMs) (Ho et al., 2020) stands out for generating high-quality samples via iterative denoising (Li et al., 2025). Hoogeboom et al. (2021) and Austin et al. (2021) paved the way for
character-level text generation with diffusion. Li et al. (2022b) uses an embedding and rounding strategy and additional classifiers for controllable text generation. Gong et al. (2022) introduces a classifier-free diffusion model for dialogue generation. Bi et al. (2023) incorporated multi-grained control signals but their multi-stage pre-training approaches increase computational costs and the difficulty of practical implementation.

As far as we are aware, we are among the first to achieve multi-task empathetic response generation using reinforcement learning within diffusion guided by psychological knowledge.

3 Methodology

Our model, ReflectDiffu, is inspired by sociological studies on emotional contagion (Hatfield et al., 1993) and empathic mirroring (De Waal and Preston, 2017), which suggest that empathy involves aligning emotional states and mimicking empathetic behavior in interpersonal interactions. Positive emotions are met with positivity, while in situations involving negative emotions, the empathetic response strategy incorporates a congruent emotional tone infused with positivity and a precise empathy intent to resonate deeply with speakers (Majumder et al., 2020; Chen et al., 2022a).

ReflectDiffu comprises two essential components: an Emotion-Contagion Encoder, enhanced with an emotional reasoning annotator for improved semantic comprehension, and a Rational Response Generation Decoder guided by an Intent Exploring-Sampling-Correcting mechanism, which mirrors human reflective dialogue behavior to enhance empathy with robustness. The architecture of our model is depicted in Figure 2.

3.1 Task Definition

The conversation history consists of multiple interactions between a user and a chatbot, represented as \( C = [c_0, c_1, \ldots, c_{n-1}] \), where \( n \) denotes the number of conversation rounds. Each utterance, \( c_i \), is tokenized into a sequence of words: \( C = [w_0^0, w_1^0, \ldots, w_0^1, w_1^1, \ldots, w_{m-1}^{n-1}] \). The primary aim is twofold: accurately discerning users’ emotional state, denoted as \( emo \), and formulating an empathetic response, \( c_n \). Additionally, We introduce two auxiliary tasks: emotion reasoning annotation and intent prediction. Within \( c_i \), emotional keywords are marked with the tag \( <em> \), while non-emotional words are labeled \( <noem> \). The chatbot also predicts the underlying conversation intent, \( intent \), based on the entire dialogue sequence \( C \).

3.2 Multi-task Emotion-Contagion Encoder

Emotion Reason Annotator. The Emotion Reason Annotator (ERA) identifies emotional cues and generates reasoning masks within conversational turns. To efficiently handle labeled data for downstream NER tasks, we follow Bogdanov et al. (2024) to utilize a LLM for data annotation and conduct distillation training with other models. ERA builds upon BERT (Devlin et al., 2019), an attention-based semantic composition network, and conditional random fields (CRF) to effectively annotate emotional phrases with tags in the sequence \( r \) consisting of \( <em> \) or \( <noem> \) and reasoning representations \( \tilde{h} \) described in Appendix C.1.

Emotion-Contagion Encoder. The Emotion-Contagion Encoder incorporates the reasoning masks learned by ERA into a transformer encoder to emulate emotional contagion.

Given that the reasoning masks \( r \) for \( <em> \) or \( <noem> \) are only applied to users’ emotional state, the chatbot’s \( r \) is always set as \( <noem> \) because empathy is user-oriented, the distinction between users’ states and system states has been made. Therefore, unlike previous methods (Sabour et al., 2022; Gao et al., 2023; Bao et al., 2024), we enhanced the context embedding by defining it as the sum of three embeddings (\( E^C \)): semantic word embedding (\( E^W \)), positional embedding(\( E^P \)), and reason embedding (\( E^R \)), incorporating the \( <em/noem> \) into the final embedding, \( E^C \), formally:

\[
E^C = E^W(C) + E^P(C) + E^R(C),
\]

where \( E^W(C), E^P(C), E^R(C) \in \mathbb{R}^{D_{emb}} \).

Then, each token \( w_i^j \) in \( E^C \) is transformed into its vector representation utilizing the context embedding \( E_C \). Following the existing methods (Gao et al., 2023; Wang et al., 2024; Hamad et al., 2024), we use a transformer encoder with one additional token, \( CTX \), prepended to gain the speaker context. The transformer encoder, denoted as TRS_{Enc}, encodes the flattened \( E^C \) into a context representation \( H \):

\[
H = \mathrm{TRS}_{\mathrm{Enc}}(E^C(C)).
\]

Finally, given the token-level context representation \( H \) and the reasoning representations \( h \) obtained from ERA, we enhance \( r \) by integrating it
Figure 2: Architecture of our model (ReflectDiffu), which comprises three primary components: Empathetic Imitation influenced by Emotional Contagion, Intent Twice: Exploring-Sampling-Correcting Mechanisms and a Response Decoder.

with \( \tilde{h} \) through an attention layer and meaning aggregation, yielding overall representation \( Q \), formally:

\[
Q = \text{mean-pooling}(Attention(H, \tilde{h})).
\] (3)

**Contrastive-Experts Emotion Classification.** Inspired by (Lin et al., 2019; Majumder et al., 2020; Chen et al., 2022b), we put forward two-expert models *C-Experts*: \( M_{pos} \) for positive emotions and \( M_{neg} \) for negative emotions to enhance emotion recognition by exploiting each model’s proficiency. Neutral emotions are addressed via a voting mechanism between experts, yielding the candidate emotion probability distribution \( p \) as follows:

\[
p =
\begin{cases}
\text{softmax}(W_{neg} \mathbf{E}_{emo} Q) & \text{if } v = n_{neg} \\
\text{softmax}(W_{pos} \mathbf{E}_{emo} Q) & \text{if } v = n_{pos} \\
\text{Voting}(W_{neg} \mathbf{E}_{emo} Q, W_{pos} \mathbf{E}_{emo} Q)) & \text{if } v = n_{neu}
\end{cases}
\] (4)

where \( v \) represents the maximum count among \( n_{neu}, n_{pos}, n_{neg} \) within a batch, based on preliminary real-time sentiment analysis via VADER (Hutto and Gilbert, 2014). \( W_{pos} \) and \( W_{neg} \) are trainable weight matrices. Voting(*) is a soft voting mechanism based on experts \( M_{pos} \) and \( M_{neg} \).

Additionally, we customize the \( n_{emo} \) NT-Xent loss (\( n_{emo} = 32 \)), denoted as \( L_{NTX} \), using pseudo labels to enhance context representation learning (Chen et al., 2020; Zheng et al., 2023) on \( Q \), while utilizing cross-entropy loss, \( L_{ce} \), for classification. Consequently, the overall loss for emotion classification, \( L_{em} \), is detailed in Appendix C.2.

3.3 Multi-task Rational Response Generation Decoder

Building on psychological works (Hatfield et al., 1993; De Waal and Preston, 2017), we propose that empathy involves mirroring users’ emotions, responding positively to positive emotions and combining support with optimism for negative states. To enhance emotional encoding and empathetic expression with controllability, we conceptualize response intentions as actions (Chen et al., 2022a; Gao et al., 2023). Unlike existing methods (Bi et al., 2023; Chen et al., 2022a) that rely on signals from externally fine-tuned classifiers, our multi-task response decoder integrates reinforcement learning into a diffusion framework (Ho et al., 2020; Gong et al., 2022) to refine intent and enhance empathy, integrating Intent Twice, Emotion-Intent Mimicry, and Response Decoding to ensure coherent and empathetic interactions.

3.3.1 Intent Twice:
Exploring-Sampling-Correction.

**Exploring:** First Intent Initialization. To enrich the contextual representation \( Q \) with extra precise intent information, we consider both internal and external factors to score each candidate intent. In particular, we fine-tune a BERT classifier on the EMPATHETICINTENT dataset (Chen et al., 2022a) offline to obtain the intent distribution \( p_{intent} \). Following a similar procedure as in Section 3.2, we compute \( p_{semantic} \) online using similarity metrics and combine the two distributions to re-rank the intents so that we can get a more accurate first intent
prediction Intentfirst:

\[
Intent_{first} = p_{semantic} + \alpha p_{intent}.
\]

Here, \( \alpha \) is a hyperparameter that balances internal and external factors.

Sampling: RL-Diffusion for Intent Twice. Inspired by (Majumder et al., 2020; De Waal and Preston, 2017), we hypothesize that empathetic behavior requires mimicking user emotions and integrating references to common emotion-corresponding actions with one’s own cognitive process when learning empathic expression(Majumder et al., 2020). Hence, the alignment between current emotions and inferred intents with universal intents, denoted as Intent_referr, is crucial for refining intention predictions, especially when errors arise in expert emotion recognition. To enhance the accuracy of action predictions and improve both controllability and effectiveness of empathetic responses, we integrate policy-based reinforcement learning (RL) within Denoising Diffusion Probabilistic Models (DDPMs)(Ho et al., 2020; Gong et al., 2022), to sample more accurate and universal intents. Our framework leverages the exploration-exploitation trade-off \( M_p \) to balance the learned intent actions and the sampling of new empathetic actions. When previous emotion recognition errors occur, intent twice mechanism can alleviate emotional misrecognition and correct wrong intents by sampling universal intents.

To define Intent_referr for reference, we perform a statistical survey for each emotion to find the top-n empathetic intention actions. The optimal value of n is 3 as shown in Table 1. We provide an experimental analysis for \( n = 3 \) in Appendix B.1.

<table>
  <tr>
    <th>Emotion</th>
    <th>Intent_referr</th>
  </tr>
  <tr>
    <td>surprised, proud, impressed, nostalgic, trusting, faithful, prepared</td>
    <td>acknowledging, encouraging, neutral</td>
  </tr>
  <tr>
    <td>excited, confident, joyful, grateful, content, caring, faithful</td>
    <td>encouraging, sympathizing, acknowledging</td>
  </tr>
  <tr>
    <td>angry, disappointed</td>
    <td>consoling, suggesting, encouraging</td>
  </tr>
  <tr>
    <td>hopeful, sentimental</td>
    <td>encouraging, wishing, consoling</td>
  </tr>
  <tr>
    <td>anticipating, lonely, afraid, anxious, guilty, embarrassed, sad, apprehensive, terrified, jealous</td>
    <td>consoling, encouraging, neutral</td>
  </tr>
  <tr>
    <td>hopeful, sentimental</td>
    <td>encouraging, wishing, consoling</td>
  </tr>
</table>

Table 1: Mapping of Emotion-Group to Top-3 Universal Intents for Reference

State Representation: Emotion Mimicry Unit. Emotion Mimicry Unit(EMU) initially splits the emotion-contagion encoding Q into positive and negative polarity representations following emotion grouping (Majumder et al., 2020), but with Intentfirst guidance in diffusion. We train two distinct DDPMs for positive-polarity representation Emo_pos with \( L_{kpso} \) and negative-polarity representation Emo_neg with \( L_{kpsn} \) following emotion-group(Majumder et al., 2020), we integrate the captured nuances of each emotional polarity with the content encoding H to obtain state Emo_fused.

Given the emotion-contagion encoding \( Q \) and a fixed step t, the diffusion process iteratively adds Gaussian noise \( \epsilon \sim \mathcal{N}(0, I) \) to Q over t steps:

\[
Q_t = \sqrt{1 - \beta_t} Q_{t-1} + \sqrt{\beta_t} \epsilon.
\]

Here, \( Q_t \) denotes emotion-contagion encoding at time step t, and \( \beta_t \in [1e-5, 5e-2] \) is the noise level at time step t. To recover the corrupted encodings \( q_t \) to their original context representation, we propose an intent-aware Conditional Variational Auto Encoder(CVAE) \( \mathcal{M}_\theta \) that predicts the noise \( \epsilon \) at each step, motivated by Park et al. (2018); Chung et al. (2022):

\[
\tilde{Q}_{t-1} = \frac{1}{\sqrt{1-\beta_t}} \left( Q_t - \frac{\beta_t \mathcal{M}_\theta(Q_t, t, Intent_{first})}{\sqrt{1 - \sum_{s=1}^t \beta_s}} \right).
\]

Here, \( \tilde{Q}_{t-1} \) represents the reconstructed encoding, and \( \theta \) denotes the parameters of \( \mathcal{M}_\theta \). Finally, we integrate with context encoding H via cross-attention to get state Emo_fused, expressed as:

\[
Emo_{fused} = \text{CrossAttention}([Emo_{pos}, H], [Emo_{neg}, H]).
\]

Action Definition: IntentTwice. The action IntentTwice involves selecting an empathetic intent from a predetermined set of intent_referr (Table 1), as determined by the policy network \( M_p \). This network comprises two linear layers and returns a probability distribution \( p_{act} \) over intent_referr. Consequently, an action is sampled in accordance with this distribution. The importance sampling ratio \( \frac{\pi(\text{IntentTwice}|Emo_{fused})}{\mu(\text{IntentTwice}|Emo_{fused})} \) is employed to rectify any discrepancies within the policy.

Reward Calculation: The reward r is calculated based on how well the selected IntentTwice aligns with the user’s emotional state e, involving two key components: a reward for positive alignment and a penalty for negative alignment, formally:
\[
R(e) = \begin{cases}
\text{sigmoid}(Emo_{pos}[i] \cdot intent_{ref}) & \text{if } is\_pos(e) \\
\text{sigmoid}(Emo_{neg}[i] \cdot intent_{ref}) & \text{otherwise}
\end{cases}.
\]
(9)

Here, \(intent_{ref}\) is the selected intent’s embedding.

Correction: Intent Adjustment Finally, the intent embeddings are updated through a shared-weight layer during *intent twice* to obtain the final intent \(intent\) with optimizing cross-entropy loss, denoted as \(L_{intent}\), ensuring consistency and effectiveness in learning and mimicking empathetic intents.

Overall, the loss for *intent twice* mechanism is represented as \(L_{twice}\):

\[
L_{twice} = L_{klpos} + L_{klneg} + L_{intent}.
\]
(10)

3.3.2 Response Decoding

Consequently, We generate the final response using the integrated response-emotion context, Emo$_{fused}$. Following Lin et al. (2019); Majumder et al. (2020); Sabour et al. (2022); Zhou et al. (2023), We apply a transformer decoder, TRS$_{dec}$ with pointer generator network $P_{Gen}(*)$, where $Emo_{fused}$ serves as both the key and value to predict the word distribution $P_w$, as detailed below:

\[
\begin{align*}
P_w &= P(R_t \mid E_{<t}, Emo_{fused}, C) \\
&= P_{Gen}(TRS_{dec}(E^C(T_{R<1}), Emo_{fused})).
\end{align*}
\]
(11)

where $E_{R<1}$ denotes the embeddings of all prior responses up to time t-1, $E^C(T_{R<1})$ indicates the embedding of the target response, and $P_{Gen}(*)$ represents the pointer generator network (See et al., 2017). TRS$_{dec}$ refers to the transformer decoder function.

3.4 Training

Lastly, all parameters of ReflectDiffu are trained jointly in an end-to-end manner to optimize the model by integrating all losses $L$, employing loss weight averaging with hyperparameters $\delta, \zeta, \eta$ as follows:

\[
L = \delta L_{em} + \zeta L_{twice} + \eta L_{res}.
\]
(12)

4 Experiments Settings

4.1 Dataset

We evaluate our approach, ReflectDiffu, using the EMPATHETICDIALOGUES dataset (Rashkin et al., 2018), which consists of 24,850 open-domain, multi-turn conversations between two interlocutors where the chatbot provides empathetic responses to the user. 32 emotion categories evenly distributed across all dialogues. Utilizing the ChatGLM4 ¹ (GLM et al., 2024; Kojima et al., 2022; Zhong et al., 2024) to annotate emotional reasoning within the EMPATHETICDIALOGUES dataset. Additionally, we utilize a fine-tuned Hugging Face model, Commonsense-QA-LLM², to reason and annotate the intents. Ultimately, we extend the original dataset with annotations for emotion reasoning, intent prediction and empathetic dialogue, adhering to the predefined 8:1:1 train/validation/test split.

4.2 Baselines

In our experiments, we compare ReflectDiffu with both classic and recent state-of-the-art (SOTA) benchmarks including MTRS (Rashkin et al., 2018) , MOEL (Lin et al., 2019) , MIME (Majumder et al., 2020) , EmpDG (Li et al., 2020) , KEMP (Li et al., 2022a) , CASE (Zhou et al., 2023) , CAB (Gao et al., 2023) and IAMM (Yang et al., 2024b). Additionally, we incorporate a comparative analysis with Harnessing (0-shot prompting) (Qian et al., 2023), as well as QWen2-7B (Yang et al., 2024a) and Llama-3.1-8B(Dubey et al., 2024), two prominent generative language models(Laskar et al., 2024). More details about baselines are shown in Appendix A.1.

4.3 Implement Details

ReflectDiffu employs 300-dimensional pre-trained GloVe vectors (Pennington et al., 2014) and follows baselines (Rashkin et al., 2018; Lin et al., 2019; Majumder et al., 2020; Li et al., 2020; Gao et al., 2023; Zhou et al., 2023) for a fair comparison. It is implemented in PyTorch 2.1.2 and trained on two NVIDIA GeForce RTX 4090 GPUs with a batch size of 32 using NoamOpt as the optimizer with learning rate warmup steps of 6000 and a learning rate decay factor of 0.01. The diffusion step is set to 1000 and the model converges after about 16000 iterations with early stopping.

4.4 Evaluation Metrics

Automatic Evaluations. To assess ReflectDiffu’s performance, we use automatic evaluation metrics for relevance, controllability, and informativeness, including *BLEU-n*, *BART$_{Score}$*, *Emo-
<table>
  <tr>
    <th rowspan="2">Models</th>
    <th colspan="4">Relevance</th>
    <th colspan="2">Controllability</th>
    <th colspan="3">Informativeness</th>
  </tr>
  <tr>
    <th>B-1 ↑</th>
    <th>B-2 ↑</th>
    <th>B-3 ↑</th>
    <th>B-4 ↑</th>
    <th>BART<sub>score</sub>↑</th>
    <th>Accemo ↑</th>
    <th>AccIntent ↑</th>
    <th>PPL ↓</th>
    <th>D-1 ↑</th>
    <th>D-2 ↑</th>
  </tr>
  <tr>
    <td>MTRS</td>
    <td>17.87</td>
    <td>8.51</td>
    <td>4.36</td>
    <td>2.61</td>
    <td>0.5173</td>
    <td>32.96</td>
    <td>-</td>
    <td>37.98</td>
    <td>0.40</td>
    <td>1.57</td>
  </tr>
  <tr>
    <td>MOEL</td>
    <td>18.02</td>
    <td>8.67</td>
    <td>4.35</td>
    <td>2.73</td>
    <td>0.5166</td>
    <td>31.02</td>
    <td>-</td>
    <td>36.81</td>
    <td>0.43</td>
    <td>1.76</td>
  </tr>
  <tr>
    <td>MIME</td>
    <td>19.82</td>
    <td>8.86</td>
    <td>4.43</td>
    <td>2.77</td>
    <td>0.5182</td>
    <td>30.26</td>
    <td>-</td>
    <td>36.93</td>
    <td>0.51</td>
    <td>1.92</td>
  </tr>
  <tr>
    <td>EmpDG</td>
    <td>19.12</td>
    <td>8.91</td>
    <td>4.89</td>
    <td>2.85</td>
    <td>0.5171</td>
    <td>32.90</td>
    <td>-</td>
    <td>37.55</td>
    <td>0.49</td>
    <td>1.65</td>
  </tr>
  <tr>
    <td>KEMP</td>
    <td>17.92</td>
    <td>8.54</td>
    <td>4.38</td>
    <td>2.71</td>
    <td>0.5232</td>
    <td>36.40</td>
    <td>-</td>
    <td>36.59</td>
    <td>0.66</td>
    <td>2.43</td>
  </tr>
  <tr>
    <td>CASE</td>
    <td>19.66</td>
    <td>8.95</td>
    <td>4.92</td>
    <td>2.90</td>
    <td>0.5336</td>
    <td>38.96</td>
    <td>-</td>
    <td>35.97</td>
    <td>0.70</td>
    <td>2.66</td>
  </tr>
  <tr>
    <td>CAB</td>
    <td>20.23</td>
    <td>9.39</td>
    <td>4.96</td>
    <td>3.01</td>
    <td>0.5392</td>
    <td>40.52</td>
    <td>-</td>
    <td>35.06</td>
    <td>0.89</td>
    <td>2.95</td>
  </tr>
  <tr>
    <td>IAMM</td>
    <td>19.51</td>
    <td>8.74</td>
    <td>4.86</td>
    <td>3.32</td>
    <td>0.5456</td>
    <td><b>43.72</b></td>
    <td>-</td>
    <td>25.94</td>
    <td>0.88</td>
    <td>3.05</td>
  </tr>
  <tr>
    <td>Harnessing (0-shot)</td>
    <td>6.57</td>
    <td>2.68</td>
    <td>1.68</td>
    <td>1.07</td>
    <td>0.3881</td>
    <td>24.40</td>
    <td>-</td>
    <td>230.99</td>
    <td><b>1.79</b></td>
    <td><b>14.72</b></td>
  </tr>
  <tr>
    <td>Qwen2-7B+CoT</td>
    <td><b>23.31</b></td>
    <td>11.21</td>
    <td>5.20</td>
    <td>3.45</td>
    <td>0.5447</td>
    <td>23.10</td>
    <td><b>41.61</b></td>
    <td>25.45</td>
    <td>0.87</td>
    <td>3.87</td>
  </tr>
  <tr>
    <td>llama-3.1-8B+CoT</td>
    <td>23.38</td>
    <td>11.29</td>
    <td>5.25</td>
    <td>3.47</td>
    <td><b>0.5480</b></td>
    <td>21.15</td>
    <td>32.02</td>
    <td><b>24.92</b></td>
    <td>0.92</td>
    <td>4.13</td>
  </tr>
  <tr>
    <td><b>ReflectDiffu</b></td>
    <td><b>23.59</b></td>
    <td><b>11.25</b></td>
    <td><b>5.35</b></td>
    <td><b>3.62</b></td>
    <td><b>0.5630</b></td>
    <td><b>48.76</b></td>
    <td><b>80.32</b></td>
    <td><b>24.56</b></td>
    <td><b>0.98</b></td>
    <td><b>4.35</b></td>
  </tr>
  <tr>
    <td>w/o ERA</td>
    <td>22.59</td>
    <td>10.66</td>
    <td>5.02</td>
    <td>3.28</td>
    <td>0.5520</td>
    <td>42.37</td>
    <td>78.68</td>
    <td>24.78</td>
    <td>0.95</td>
    <td>4.27</td>
  </tr>
  <tr>
    <td>w/o C-Experts</td>
    <td>23.13</td>
    <td>11.05</td>
    <td>5.06</td>
    <td>3.31</td>
    <td>0.5619</td>
    <td>39.44</td>
    <td>77.44</td>
    <td>24.82</td>
    <td>0.91</td>
    <td>4.03</td>
  </tr>
  <tr>
    <td>w/o Intent twice</td>
    <td>20.91</td>
    <td>9.86</td>
    <td>4.87</td>
    <td>3.16</td>
    <td>0.5436</td>
    <td>44.56</td>
    <td>66.44</td>
    <td>29.25</td>
    <td>0.85</td>
    <td>3.97</td>
  </tr>
  <tr>
    <td>w/o EMU</td>
    <td>21.95</td>
    <td>10.05</td>
    <td>4.96</td>
    <td>3.22</td>
    <td>0.5490</td>
    <td>48.35</td>
    <td>79.24</td>
    <td>27.45</td>
    <td>0.69</td>
    <td>2.96</td>
  </tr>
</table>

Table 2: Results of automatic evaluations and ablation study. Metrics include BLEU-1 to BLEU-4 (B-1 B-4), BARTScore for Relevance; emotion and intent accuracy (Accemo, AccIntent) for Controllability; perplexity (PPL) and distinct-n (D-1, D-2) for Informativeness.

tion Accuracy Accemo, Intent Accuracy Accintent, Distinct-1, Distinct-2, and Perplexity PPL (see Appendix A.2 for details).

Human Evaluation. For human evaluation, we conduct A/B testing on empathy, relevance, and fluency with three recruited annotators and a supervisory LLM to resolve disagreements (see Appendix A.2 for details). We compare ReflectDiffu against a selection of widely adopted baselines and recent generation models to ensure consistency and interpretability in human judgments. For clarity and consistency, models relying heavily on memory mechanisms (IAMM) or general-purpose prompting strategies (Harnessing) are excluded from this evaluation.

5 Results and Discussion

Automatic Evaluation Results As shown in table 2, our model, ReflectDiffu, outperforms all baseline models and significantly enhances all metrics. Compared with empathy-specific models(Rashkin et al., 2018; Lin et al., 2019; Majumder et al., 2020; Li et al., 2020, 2022a; Zhou et al., 2023; Gao et al., 2023), which mainly explore the connection between emotion states and empathetic contexts but ignore the internal mechanisms of emotional causes, emotions, and intents and only rely on inferred external knowledge, resulting in suboptimal empathetic controllability (low emotion accuracy Accemo), weak similarity and coherence with the empathetic ground truth (indicated by low BLEU-n and BARTscore), and a lack of diversity (implied by low Distinct-1 and Distinct-2). In contrast, ReflectDiffu exhibits remarkable superiority, exceeding the best baseline, CAB, approximately in BLEU-1, BLEU-2, BLEU-3, BLEU-4, BARTscore, Accemo by 16.6%, 20%, 8.1%, 20.3%, 4.6% and 20.3% respectively for Emotion-Contagion Encoder to enhance semantic understanding and achieve 80.32% intent accuracy for its intent twice mechanism. Moreover, ReflectDiffu shows improvements of approximately 30.1% in PPL, 10.1% in Distinct-2, and 47.4% in Distinct-2 compared with CAB for Diffusion within intent guidance. Compared with Harnessing (0-shot), which performs poorly across relevance, controllability, and fluency (e.g., BLEU-1: 6.57, Accemo: 24.40, PPL: 230.99), ReflectDiffu achieves substantially higher scores while maintaining coherence and diversity, demonstrating its robustness in zero-shot empathetic dialogue. Compared to IAMM, which excels in emotion accuracy (43.72) and diversity (Distinct-2: 3.05) but lacks intent controllability and suffers from higher perplexity, ReflectDiffu achieves superior balance across all dimensions, with higher BLEU scores, better controllability (Accintent: 80.32), and lower PPL. These results highlight ReflectDiffu’s effectiveness in generating empathetic, coherent, and diverse responses.

Moreover, compared with llama-3.1-8B(Dubey
et al., 2024) with Chain-of-Thought(CoT) via fewshots (a SOTA LLM-based empathetic dialogue model in our experiments). ReflectDiffu outperforms llama-3.1-8B obviously by 1.90%, 4.32%, 2.73%, 130.54%, 150.94%, 6.52% and 5.32% in BLEU-3, BLEU-4, BARTScore, Accemo, Accintent, Distinct-1 and Distinct-2. Higher BARTScore, Accemo and Accintent robustly underscore the efficacy of ReflectDiffu in fostering empathy. Lower PPL and higher Distinct-1 and Distinct-2 further corroborate the empathetic diversity that ReflectDiffu can engender.

Human Evaluation Results Table 3 presents the results of the human A/B testing, comparing ReflectDiffu with various baseline models across three criteria: empathy (Emp.), relevance (Rel.), and fluency (Flu.). The evaluations reveal that ReflectDiffu consistently outperforms the baseline models across all criteria.

<table>
  <tr>
    <th>Comparison</th>
    <th colspan="3">Aspects</th>
    <th>Win</th>
    <th>Lose</th>
    <th>Tie</th>
  </tr>
  <tr>
    <td rowspan="3">ReflectDiffu vs. MTRS</td>
    <td>Emp.</td>
    <td colspan="3">51.1</td>
    <td>18.0</td>
    <td>30.9</td>
  </tr>
  <tr>
    <td>Rel.</td>
    <td colspan="3">48.1</td>
    <td>17.5</td>
    <td>34.4</td>
  </tr>
  <tr>
    <td>Flu.</td>
    <td colspan="3">40.1</td>
    <td>11.7</td>
    <td>48.2</td>
  </tr>
  <tr>
    <td rowspan="3">ReflectDiffu vs. MOEL</td>
    <td>Emp.</td>
    <td colspan="3">45.4</td>
    <td>21.2</td>
    <td>33.4</td>
  </tr>
  <tr>
    <td>Rel.</td>
    <td colspan="3">37.3</td>
    <td>22.5</td>
    <td>40.2</td>
  </tr>
  <tr>
    <td>Flu.</td>
    <td colspan="3">31.4</td>
    <td>13.7</td>
    <td>54.9</td>
  </tr>
  <tr>
    <td rowspan="3">ReflectDiffu vs. MIME</td>
    <td>Emp.</td>
    <td colspan="3">50.3</td>
    <td>20.8</td>
    <td>28.9</td>
  </tr>
  <tr>
    <td>Rel.</td>
    <td colspan="3">43.7</td>
    <td>19.2</td>
    <td>37.1</td>
  </tr>
  <tr>
    <td>Flu.</td>
    <td colspan="3">38.4</td>
    <td>9.1</td>
    <td>52.5</td>
  </tr>
  <tr>
    <td rowspan="3">ReflectDiffu vs. EmpDG</td>
    <td>Emp.</td>
    <td colspan="3">52.2</td>
    <td>19.8</td>
    <td>27.9</td>
  </tr>
  <tr>
    <td>Rel.</td>
    <td colspan="3">50.8</td>
    <td>16.5</td>
    <td>32.7</td>
  </tr>
  <tr>
    <td>Flu.</td>
    <td colspan="3">36.4</td>
    <td>10.3</td>
    <td>53.3</td>
  </tr>
  <tr>
    <td rowspan="3">ReflectDiffu vs. KEMP</td>
    <td>Emp.</td>
    <td colspan="3">55.2</td>
    <td>23.1</td>
    <td>21.7</td>
  </tr>
  <tr>
    <td>Rel.</td>
    <td colspan="3">62.4</td>
    <td>29.8</td>
    <td>7.8</td>
  </tr>
  <tr>
    <td>Flu.</td>
    <td colspan="3">35.7</td>
    <td>13.3</td>
    <td>51.0</td>
  </tr>
  <tr>
    <td rowspan="3">ReflectDiffu vs. CAB</td>
    <td>Emp.</td>
    <td colspan="3">53.6</td>
    <td>22.4</td>
    <td>24.0</td>
  </tr>
  <tr>
    <td>Rel.</td>
    <td colspan="3">56.1</td>
    <td>24.6</td>
    <td>19.3</td>
  </tr>
  <tr>
    <td>Flu.</td>
    <td colspan="3">32.3</td>
    <td>10.2</td>
    <td>57.5</td>
  </tr>
  <tr>
    <td rowspan="3">ReflectDiffu vs. CASE</td>
    <td>Emp.</td>
    <td colspan="3">52.0</td>
    <td>15.0</td>
    <td>33.0</td>
  </tr>
  <tr>
    <td>Rel.</td>
    <td colspan="3">45.5</td>
    <td>25.0</td>
    <td>29.5</td>
  </tr>
  <tr>
    <td>Flu.</td>
    <td colspan="3">49.0</td>
    <td>27.1</td>
    <td>23.9</td>
  </tr>
  <tr>
    <td rowspan="3">ReflectDiffu vs. Qwen2-7B+CoT</td>
    <td>Emp.</td>
    <td colspan="3">52.5</td>
    <td>22.2</td>
    <td>25.3</td>
  </tr>
  <tr>
    <td>Rel.</td>
    <td colspan="3">53.1</td>
    <td>25.3</td>
    <td>21.6</td>
  </tr>
  <tr>
    <td>Flu.</td>
    <td colspan="3">41.2</td>
    <td>12.5</td>
    <td>46.3</td>
  </tr>
  <tr>
    <td rowspan="3">ReflectDiffu vs. llama-3.1-8B+CoT</td>
    <td>Emp.</td>
    <td colspan="3">51.2</td>
    <td>21.8</td>
    <td>27.0</td>
  </tr>
  <tr>
    <td>Rel.</td>
    <td colspan="3">54.4</td>
    <td>24.5</td>
    <td>21.1</td>
  </tr>
  <tr>
    <td>Flu.</td>
    <td colspan="3">33.8</td>
    <td>18.5</td>
    <td>47.7</td>
  </tr>
</table>

Table 3: Human A/B evaluation results between ReflectDiffu and baselines.

Ablation Study. As shown in Table 2, we conducted four ablation studies to evaluate the key components of our model: (1) w/o ERA: Removing the Emotion Reason Annotator (ERA) that improves emotion understanding with reasoning masks; (2) w/o C-Experts: Excluding the Contrastive-Experts for emotion classification; (3) w/o Intent twice: Eliminating the Intent Exploring-Sampling-Correcting mechanism; and (4) w/o EMU: Lacking the Emotion Mimicry Unit (EMU) with DDPMs for state representation.

Effect of ERA. Excluding Emotion Reason Annotator(ERA) designed to improve emotion understanding by reasoning masks leads to a significant decrease in BLEU-n, BARTScore and Accemo, indicating w/o ERA compromises emotion perception and thereby results in inferior empathetic responses’ relevance and quality.

Effect of C-Experts. Removing Contrastive-Experts C-Experts leads to a notable decline in Accemo from 48.76 to 39.44, indicating that w/o C-Experts deteriorates the ability to classify emotions, consequently negatively affecting the controllability of empathy, making it harder to precisely match responses with desired emotional states.

Effect of Intent twice. Eliminating the Intent Exploring-Sampling-Correcting mechanism significantly reduced Accintent from 80.32 to 66.44, along with poor BLEU-n and BARTscore, higher PPL, w/o Intent twice impairs the model’s ability to accurately capture and fulfill response intent, weakening empathetic responses’ relevance and quality.

Effect of EMU. Lacking the Emotion Mimicry Unit (EMU) for state representation results in a considerable decrease in BLEU-n, Distinct-1 and Distinct-2, along with PPL, indicating that w/o EMU negatively affects the quality and distinctiveness of empathetic responses.

Case Study. In this case (Table 4), ReflectDiffu shows improved empathetic response generation by identifying and mimicking the user’s emotional state. Using the Intent Exploring-Sampling-Correcting mechanism, the model refines its initial intent from encouraging to consoling, resulting in a more supportive reply. Compared to baselines, ReflectDiffu better aligns with users’ emotions, offers a clear and empathetic follow-up, enhancing interaction quality. (Details on mitigating emotion recognition errors are provided in Appendix B.1.)

6 Conclusion

In this paper, we propose ReflectDiffu, a novel psychological multi-task framework for empathetic dialogue that integrates Emotion-Contagion Encoder and Response Generation Decoder guided by an Intent Twice mechanism to better understand
<table>
  <tr>
    <th>Emotion</th>
    <td>Terrified</td>
  </tr>
  <tr>
    <th>Context</th>
    <td>Yeah about 10 years ago I had a horrifying experience. It was 100% their fault but they hit the water barrels and survived. They had no injuries but they almost ran me off the road.</td>
  </tr>
  <tr>
    <th>MTRS</th>
    <td>that is pretty scary ! i am glad you are ok .</td>
  </tr>
  <tr>
    <th>MOEL</th>
    <td>that is so terrible! i am so sorry.</td>
  </tr>
  <tr>
    <th>MIME</th>
    <td>oh no ! i am so sorry to hear that .</td>
  </tr>
  <tr>
    <th>EmpDG</th>
    <td>oh no , i am so sorry to hear that .</td>
  </tr>
  <tr>
    <th>KEMP</th>
    <td>oh no ! i hope you are okay .</td>
  </tr>
  <tr>
    <th>CASE</th>
    <td>i hope you can get it fixed. Are you okay now?</td>
  </tr>
  <tr>
    <th>CAB</th>
    <td>I hope you are able to get it fixed,and hope you are ok!</td>
  </tr>
  <tr>
    <th>IAMM</th>
    <td>That sounds really scary and I’m glad you’re okay.</td>
  </tr>
  <tr>
    <th>Harnessing</th>
    <td>So scary! Glad you’re okay—stay safe out there.</td>
  </tr>
  <tr>
    <th>Qwen2-7B+CoT</th>
    <td>I’m sorry to hear about your experience. It sounds stressful and dangerous.</td>
  </tr>
  <tr>
    <th>llama3.1-8B+CoT</th>
    <td>I’m sorry to hear that. If you want, you can talk more about it.</td>
  </tr>
  <tr>
    <th>ReflectDiffu</th>
    <td><i>Intent<sub>first</sub>: encouraging ×</i><br><i>Intent<sub>twice</sub>: consoling ✓</i><br>oh no! That sounds absolutely terrifying . I hope you were not hurt, Were you injured ?</td>
  </tr>
  <tr>
    <th>Golden</th>
    <td>Did you suffer any injuries?</td>
  </tr>
</table>

Table 4: Case study comparison between ReflectDiffu and baselines.

users’ emotional states, predict intents accurately, and generate highly intent-aligned empathetic responses. Both automated and human evaluations demonstrate that ReflectDiffu excels in relevance, controllability, and informativeness of empathetic dialogue. Our research may inspire future studies on modeling emotion-intent interaction in human discourse and other linguistic behaviors.

Limitations

Our ReflectDiffu framework, integrating emotion contagion and intent prediction mechanisms with the Intent Twice mechanism, has performed exceptionally in both automatic and human evaluations, significantly enhancing the relevance, controllability, and informativeness of empathetic responses.

We discuss the primary limitation of this work as follows: The integration of Denoising Diffusion Probabilistic Models (DDPMs) and reinforcement learning mechanisms has augmented the computational requirements for training, presenting challenges for deployment in resource-constrained settings or on devices with limited capabilities. To alleviate this limitation, we have adopted reparameterization and multi-task techniques for optimization. As a result, the overall training time is notably shorter than that of multi-stage LLM (Chen et al., 2024; Yang et al., 2024a; Dubey et al., 2024) while achieving state-of-the-art outcomes.

In conclusion, despite the existing limitations, ReflectDiffu is relatively lightweight compared to LLM. Moreover, our ongoing research efforts aim to achieve lightweight quantization to accelerate the model’s implementation and collaboration.

Ethical Considerations

Our research utilizes the EMPATHETICDIALOGUES dataset Rashkin et al. (2018), an open-source resource devoid of any personal privacy information. To annotate the data for emotion reasoning and intent prediction, we leverage prompts techniques (Kojima et al., 2022) and LLM contrastive voting mechanisms (Zhong et al., 2024) to label intent and emotional reason, thereby minimizing human bias and reducing the risk of model hallucination. Our human evaluations are conducted by three professional annotators, who operate anonymously to protect privacy and ensure objective assessments following our instructions (refer to Appendix D). Annotators are compensated fairly for their contributions.

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A Experimental Details

A.1 Baselines

In our experiments, we compare ReflectDiffu with both classic and recent state-of-the-art (SOTA) benchmarks.

• Multitask-Transformer(MTRS):Rashkin et al. (2018) introduced a Transformer model trained for both sentiment detection and empathetic response generation.
• MOEL: Lin et al. (2019) proposed a Transformer model with 32 emotion-specific decoders and a meta-listener to generate contextually appropriate responses.
• MIME: Majumder et al. (2020) combined a Transformer with a VAE to generate empathetic responses by mimicking user emotions through polarity-based clustering and stochastic emotion mixtures.
• EmpDG: Li et al. (2020) used a Transformer with a WGAN to capture emotional nuances via a token-level perception mechanism.
• KEMP: Li et al. (2022a) proposed leveraging external knowledge, including commonsense and emotional lexical knowledge, to enhance empathetic dialogue generation.
• CASE: Zhou et al. (2023) integrated a commonsense cognition graph and an emotional concept graph to align user cognition and affection for empathetic responses.
• CAB: Gao et al. (2023) integrated cognition, affection, and behavior to enhance empathetic dialogue generation.
• IAMM: Yang et al. (2024b) improves empathetic response quality by modeling internal affect memory and multi-level affective matching. It is designed to enhance emotional alignment and content diversity.
• Harnessing (0-shot): Qian et al. (2023) leverages GPT-4o to generate empathetic responses via zero-shot setting under 30 budget tokens.
• QWen2-7B + CoT: We fine-tune QWen2-7BYang et al. (2024a), and then employ Chain-of-Thought (CoT) to infer emotion, intent, and generate empathetic responses for improved empathy.
• llama3.1-8B + CoT: We fine-tune llama3.1-8BDubey et al. (2024), and then employ Chain-of-Thought (CoT) to infer emotion, intent, and generate empathetic responses for improved empathy.

A.2 Evaluations Metrics

Automatic Evaluation. To assess ReflectDiffu’s performance, we use automatic evaluation metrics in three categories: relevance, controllability, and informativeness.

• Relevance: We use BLEU (Papineni et al., 2002) and BARTScore(Yuan et al., 2021) to measure similarity between generated and reference texts. Higher scores indicate more relevant outputs.
• Controllability: This is measured by Emotion Accuracy (\( Acc_{emo} \)) and Intent Accuracy (\( Acc_{intent} \)), which check the model’s ability to detect emotions and recognize user intent.
• Informativeness: Evaluated using Distinct-1, Distinct-2 (Li et al., 2016), and Perplexity (PPL) (Serban et al., 2015).
  – Distinct-N: Measures the proportion of unique unigrams and bigrams, indicating diversity. Higher scores show more varied responses.
  – Perplexity (PPL): Lower PPL scores indicate better performance, as the model predicts the next word more accurately, resulting in more fluent and coherent text.

Human Evaluation. Following Zhou et al. (2023); Gao et al. (2023); Wang et al. (2024), we conduct Human A/B testing between response pairs based on the following criteria evaluated by three annotators: (1) Empathy (Emp.): Assessing the model’s ability to generate empathetic content, including understanding the user’s emotional state and responding appropriately. (2) Relevance (Rel.): Determining how well the model’s responses relate to the dialog history, ensuring coherence and logical progression. (3) Fluency (Flu.): Evaluating the naturalness and readability of the replies, including grammatical correctness and ease of understanding. To ensure fair scoring, we introduced a supervisory LLM, ChatGPT\(^3\) inspired by Zheng et al. (2024). In cases of significant disagreement among annotators, ChatGPT provided the final rating.

B Additional Experiments

B.1 Explanation of the hyperparameter \( n \) of intent\(_{infer}\)

After annotating the dataset with empathetic intentions, we conducted a statistical analysis to determine the frequency of each intention for every emotion. Figure 3 illustrates this data, where rows represent distinct emotions and columns represent specific empathetic intentions. The color intensity in each cell indicates the relative frequency of a particular intention corresponding to an emotion, with darker shades signifying higher frequencies. Figure 3 aids in understanding the predominant empathetic actions associated with each emotional state, thereby providing insights into the alignment of universal intents (Intent\(_{refer}\)) with user emotions. We observed that setting \( n = 3 \) effectively avoids

3https://chat.openai.com
non-universal intentions while ensuring that, besides the neutral intent, a more meaningful intent is sampled within the top-2 Intent-refer.

![Heatmap of Relative Frequencies of Empathetic Intentions for Each Emotion.](page_184_370_627_312.png)

Figure 3: Heatmap of Relative Frequencies of Empathetic Intentions for Each Emotion.

B.2 Case Study in Misclassification

We deliberately selected a ReflectDiffu emotion recognition error case to validate the effectiveness of our reflection mechanism. Table 5 compares responses from various models, including MOEL, MIME, EmpDG, KEMP, CASE, CAB, IAMM, Harnessing, Qwen2-7B+CoT, llama3.1-8B+CoT and ReflectDiffu, to a user’s context of feeling hopeful after applying for graduate school. Initially, ReflectDiffu misidentified the emotion as "joyful" and the intent as "acknowledging." However, after employing the reflection mechanism, it correctly identified the intent as "encouraging." This demonstrates the model’s capability to correct errors and generate more empathetic responses through its reflection mechanism.

<table>
  <tr>
    <th>Emotion</th>
    <th>hopeful</th>
  </tr>
  <tr>
    <th>Context</th>
    <td>i just applied for graduate school ! i feel good about my chances !</td>
  </tr>
  <tr>
    <th>MTRS</th>
    <td>i hope you have a great time !</td>
  </tr>
  <tr>
    <th>MOEL</th>
    <td>i am sure you will do great ,</td>
  </tr>
  <tr>
    <th>MIME</th>
    <td>i am sure you will do great !</td>
  </tr>
  <tr>
    <th>EmpDG</th>
    <td>that is great ! i hope you are going to school for a new one ?</td>
  </tr>
  <tr>
    <th>KEMP</th>
    <td>that is great ! i hope you get it .</td>
  </tr>
  <tr>
    <th>CASE</th>
    <td>That is good, I am glad you are able to get it</td>
  </tr>
  <tr>
    <th>CAB</th>
    <td>That is awesome! glad you are better !</td>
  </tr>
  <tr>
    <th>IAMM</th>
    <td>Perfect! Graduate school applications are exciting and I hope it works out for you.</td>
  </tr>
  <tr>
    <th>Harnessing</th>
    <td>That's great! Hope it goes well for you.</td>
  </tr>
  <tr>
    <th>Qwen2-7B+CoT</th>
    <td>That's wonderful! Applying for graduate school is a significant step</td>
  </tr>
  <tr>
    <th>llama3.1-8B+CoT</th>
    <td>Congratulations on taking this important step! That's fantastic!</td>
  </tr>
  <tr>
    <th>ReflectDiffu</th>
    <td><i>emotion:</i> joyful ×<br><i>Intent<sub>first</sub></i> : acknowledging ×<br><i>Intent<sub>nicer</sub></i> : consoling√<br>i am proud and sure you’ll do just fine in school.</td>
  </tr>
  <tr>
    <th>Golden</th>
    <td>I’m so proud of you! I’ll pray for your success!</td>
  </tr>
</table>

Table 5: Case study in misclassification comparison between ReflectDiffu and baseline models.

in the \( j \)-th utterance, we use a pretrained BERT model to obtain contextualized token representations \( h_j^i \):

\[
h_j^i = \text{BERT}(w_j^i).
\]

where \( h_j^i \) is the hidden state output by BERT corresponding to the token \( w_j^i \).

Unlike traditional Named Entity Recognition (NER) models (Souza et al., 2019; Qi and Qin, 2023), we introduce an attention-based semantic composition network that progressively distinguishes between binary sets of words.

Each token representation \( h_j^i \) is initially treated as a word-level feature representation. The attention network computes the correlation between pairs of word vectors \( h_j^i \) and \( h_m^k \). The relevance score \( \alpha_{ik}^{jm} \) and reasoning representation \( \tilde{h} \) are defined as:

\[
\alpha_{ik}^{jm} = \frac{\exp(\text{Attention}(h_j^i, h_m^k))}{\sum_{k,m} \exp(\text{Attention}(h_j^i, h_k^m))},
\]
\[
\tilde{h}_j^i = \sum_{k,m} \alpha_{ik}^{jm} h_m^k.
\]

where \( \tilde{h}_j^i \) is the attention-weighted representation for the token \( w_j^i \), enriched with contextual informa-

C Implement Details

C.1 Emotion Reason Annotator

Our approach leverages BERT (Devlin et al., 2019), an attention-based semantic composition network, and conditional random fields (CRF) to effectively annotate emotional phrases with tags such as \( <em> \) or \( <noem> \). Specifically, given the token-level dialogue history \( C \), where \( w_i^j \) represents the \( i \)-th token
tion from related tokens within the conversational turn.

Finally, the enriched representations are passed through a CRF layer to obtain the final predictions:

\[
P(\mathbf{r} \mid \tilde{\mathbf{h}}) = \frac{\exp \left( \sum_{j=1}^n \left( A_{r_{j-1}, r_j} + \mathbf{W} r_j h_j' \right) \right)}{\sum_{r' \in \mathcal{R}(\tilde{\mathbf{h}})} \exp \left( \sum_{j=1}^n \left( A_{r'_{j-1}, r'_j} + \mathbf{W} r'_j h_j' \right) \right)}
\]

where \( \mathbf{r} = (r_1, r_2, \ldots, r_n) \) represents the sequence of reasoning labels, each \( y_i \in \{<sem>, <noem>\} \), \( h_j' \) is the reasoning representation, \( A \) is the transition matrix, and \( \mathbf{W} \) represents the weights for the CRF layer.

C.2 Definition of \( L_{\text{em}} \)

Inspired by (Chen et al., 2020; Zheng et al., 2023), we use the NT-Xent loss (\( n_{emo} = 32 \)) \( L_{\text{NTX}} \) and cross-entropy loss (\( L_{\text{ce}} \)) for contrastive emotion classification (\( L_{\text{em}} \)), formally:

\[
L_{\text{NTX}}^{(n_{emo})^i} = - \sum_{i=1}^n \sum_{\substack{j=1 \\ j \neq i}}^n \mathbb{1}_{[y_i = y_j]} \log \frac{\exp(s(i, j))}{\sum_{k=1}^{32} \exp(s(i, k))},
\]
\[
L_{\text{NTX}} = \sum_{i=1}^{32} L_{\text{NTX}}^{(n_{emo})^i},
\]
\[
L_{\text{ce}} = - \log \mathcal{P}[e],
\]
\[
L_{\text{em}} = L_{\text{NTX}} + L_{\text{ce}}.
\]

Here, \( n \) represents the number of samples, \( y_i \) is the pseudo-label of the \( i \)-th sample, \( \mathbb{1}_{[y_i = y_j]} \) is an indicator function that equals 1 if \( y_i = y_j \) and 0 otherwise, \( s(i, j) \) denotes the similarity between samples \( i \) and \( j \), and \( \mathcal{P}[e] \) is the predicted probability for the true emotion class \( e \).

D Annotators Instructions for Human Evaluation

Professional annotators received our detailed guidelines to guarantee high-quality and unbiased evaluations.

• Evaluation Criteria: Annotators assessed responses based on three key criteria:
  – Empathy (Emp.): Evaluators were instructed to assess how well the response understood and mirrored the user’s emotional state. Examples of high empathy included responses that acknowledged the user’s feelings and provided appropriate support or encouragement. Low empathy responses were those that failed to recognize or appropriately respond to the user’s emotions.
  – Relevance (Rel.): This criterion focused on how well the response related to the previous conversation context. High relevance responses directly addressed the user’s statements or questions, maintaining coherence. Low relevance responses were off-topic or did not logically follow the conversation flow.
  – Fluency (Flu.): Evaluators assessed the grammatical correctness and naturalness of the responses. Fluent responses were well-structured, easy to read, and free of grammatical errors. Non-fluent responses contained grammatical mistakes, awkward phrasing, or were difficult to understand.

• Conflict Resolution: Procedures were established to handle disagreements among annotators:
  – When annotators disagreed on the evaluation of a response, a discussion was initiated to reach a consensus.
  – If consensus could not be achieved, a supervisory Large Language Model (LLM) provided the final rating to ensure objective and consistent evaluations across different cases.

• Anonymity and Privacy: Annotators were assured that their evaluations would be anonymized to protect their identities. They were informed that their personal information would not be shared or disclosed in any part of the study, ensuring their privacy and confidentiality.
• Compensation and Acknowledgment: Annotators were informed about their compensation:
  – They were fairly compensated for their time and effort in evaluating the responses.
  – Their contributions would be acknowledged in the final publication of the study to recognize their important role in the research process.

Reviewer paper summaries: The paper proposes ReflectDiffu, a lightweight framework for empathetic response generation. It leverages emotion contagion to boost emotional expressiveness, uses an emotion reasoning mask to identify key emotional cues, and integrates intent mimicry via reinforcement learning to refine response generation. Experiments demonstrate that ReflectDiffu outperforms current models in relevance, controllability, and informativeness, achieving improved performance in both automatic and human evaluations.

The paper introduces ReflectDiffu, a novel lightweight framework for empathetic response generation that captures the interplay between emotional contagion and intent mimicry. Inspired by psychological theories, it integrates an Emotion-Contagion Encoder and a Response Decoder guided by an Intent-Twice (Exploring–Sampling–Correcting) mechanism. The model also introduces a multi-task dataset including emotion reasoning and intent prediction, using LLM-assisted annotation. ReflectDiffu employs reinforcement learning within a diffusion model to align emotional understanding with response generation. Extensive experiments on the EMPATHETICDIALOGUES dataset show that ReflectDiffu outperforms state-of-the-art models in empathy, relevance, and controllability while being computationally more efficient than LLM-heavy approaches.

In this paper, author proposes a lightweight framework combining reinforcement learning and diffusion models, ReflectDiffu, which uses emotional contagion and mimics dialogue styles and emotions to generate empathetic responses and achieve stronger emotional relevance and diversity of responses.
Summary of strengths: (1) The paper presents an interesting motivation by enhancing empathetic ability through the simultaneous consideration of emotional contagion and empathetic mimicry.

(2) The proposed Exploring-Sampling-Correction method is innovative.

- ReflectDiffu uniquely captures the interplay between emotional states and communicative intent, addressing a major gap in empathetic response generation. By modeling intent explicitly and aligning it with emotion through the Intent-Twice mechanism, the framework improves the controllability and precision of empathetic replies—something prior models often overlook.
- The proposed Exploring–Sampling–Correcting loop enables the model to reflect on and refine initial intent predictions, especially in cases of emotional misrecognition. This self-correcting approach enhances both the accuracy of intent interpretation and the flexibility of emotional expression, leading to more nuanced and context-appropriate responses.

1. A novel approach is proposed, which uses a combination of reinforcement learning and diffusion models to achieve affective alignment, continuously optimizing while ensuring the diversity of responses.

2. By using the emotion-intention contagion module to capture the nuances of emotional intention in dialogue, the model is endowed with the ability to flexibly identify emotional information.

3. The model demonstrates consistent performance improvements compared to existing baselines.
Summary of weaknesses: (1) The introduction is unclear, especially regarding existing work, and several typical methods are missing from the related work section. 

(2) The paper also lacks necessary explanations for key concepts and details—for example, the "reflect mechanism," whether additional knowledge is used, and the scale of the model's parameters. 

(3) Moreover, the proposed method relies solely on a small-scale model, which may limit its expressive capacity.

1. The paper lacks clarity on the implementation of key baselines, particularly the two LLM-based models using Chain-of-Thought (CoT) prompting. These models are central to the comparative analysis, yet essential information is missing. Including prompt templates would significantly improve reproducibility and strengthen the validity of the comparison.
2. Key aspects of the human evaluation methodology are not reported, making it difficult to assess the reliability of the results:
- How many participated? Were they native speakers? What was their linguistic or psychological background?
- Where was the evaluation conducted (e.g., crowdsourcing, expert panel, controlled experiment) and under what conditions?
- Without IAA scores, it’s unclear how consistent the human evaluations were, making it difficult to justify why human ratings contradict ACC scores in automatic evaluation.
- The notion of “significant disagreement” in A/B testing is poorly defined, and the use of an LLM to resolve ties is unconventional and unexplained. It raises concerns about transparency and objectivity in human evaluation.

1. ReflectDiffu's high performance depends on high-quality training datasets and may be limited in noisy scenarios.  In addition, LLM is used to label affective reasoning cognition when expanding the dataset, which may introduce noise and LLM's own bias and affect the stability.

2. In this paper, sufficient ablation experiments were conducted to verify the superior effect of the method, but the analysis of model empathy failures or weak samples was not set up.  Supplementary qualitative analysis may have some significance for further improving the internal decision-making process of the model.

3. The paper lacks sufficient discussion of related work with similar approaches, such as [1], which also investigates the "internal mechanisms behind emotional causes, emotions, and intent." This omission weakens the claimed contributions.

[1] ECC: Synergizing Emotion, Cause and Commonsense for Empathetic Dialogue Generation
Comments, suggestions and typos: (1) In the introduction, the summary of existing work is somewhat confusing. The paper first discusses existing methods, then introduces LLM–based approaches, followed by small-scale empathetic models, leading to a lack of clarity and logical coherence. Additionally, the discussion of existing work in the introduction is incomplete. Some typical methods are omitted,  such as EmpSOA [1], DiffusEmp [2], dynamic knowledge–based methods [3], IAMM [4], and LLM–based approaches [5].

(2) In the baseline comparisons, the authors did not include baselines known for strong emotional capabilities (e.g., EmpSOA, dynamic knowledge–based methods [3], IAMM). The rationale behind this omission is unclear.

(3) It is not specified whether the proposed method incorporates additional external knowledge, such as COMET knowledge, which could significantly impact model performance. Moreover, the emotional accuracy of the BART model in the Emotion Reason Annotator after training is not provided.

(4) The paper refers to a "reflect mechanism" but does not provide a detailed explanation of its meaning or how it relates to emotional contagion and empathetic mimicking.

(5) The model's batch size is set to 32, while the baseline uses 16, leading to an unfair comparison. It is unclear how the proposed method would perform under identical conditions. Furthermore, details such as the number of transformer layers and the specific pre-trained model used are not provided.

(6) In the ablation experiments, the removal of C-Experts leads to a significant drop in emotional accuracy, which is counterintuitive given that these modules contain only a few parameters and have limited learning capacity.

(7) Small-scale models have limited response generation capability compared to large language models. The authors did not use a large language model, raising concerns about the model's expressive capacity.

[1] 23ACL_Don’t Lose Yourself! Empathetic Response Generation via Explicit Self-Other Awareness

[2] 23ACL_DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation

[3] 23ACL_Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge

[4] 24ACL_An Iterative Associative Memory Model for Empathetic Response Generation

[5] 23EMNLP_Harnessing the power of large language models for empathetic response generation: Empirical investigations and improvements

See my comments above

Although the paper is exceptionally well-written overall, the clarity of the methods section could be improved; it is currently overly complex.
Overall assessment scores: 3.5

3

3.5