Beyond Verbal Cues: Emotional Contagion Graph Network for Causal Emotion Entailment
Authors: Fangxu Yu, Junjie Guo, Zhen Wu, Xinyu Dai
Accepted at: acl25

Beyond Verbal Cues: Emotional Contagion Graph Network for Causal Emotion Entailment

Fangxu Yu    Junjie Guo    Zhen Wu*    Xinyu Dai
National Key Laboratory for Novel Software Technology, Nanjing University, China
School of Artificial Intelligence, Nanjing University, China
{yufx, guojj}@smail.nju.edu.cn
{wuz, daixinyu}@nju.edu.cn

Abstract

Emotions are fundamental to conversational understanding. While significant advancements have been achieved in conversational emotion recognition and emotional response generation, recognizing the causes of eliciting emotions is less explored. Previous studies have primarily focused on identifying the causes of emotions by understanding verbal contextual utterances, overlooking that non-verbal emotional cues can elicit emotions. To address this issue, we develop an Emotional Contagion Graph Network (ECGN) that simulates the impact of non-verbal implicit emotions on the counterpart’s emotions. To achieve this, we construct a heterogeneous graph that simulates the transmission of non-verbal emotions alongside verbal influences. By applying message passing between nodes, the constructed graph effectively models both the implicit emotional dynamics and explicit verbal interactions. We evaluate ECGN’s performance through extensive experiments on the benchmark datasets and compare it against multiple state-of-the-art models. Experimental results demonstrate the effectiveness of the proposed model. Our code is available at https://github.com/Yu-Fangxu/ECGN.

1 Introduction

Emotions are widely present in human communication. It is crucial for humans to infer others’ thoughts that are accompanied by changes in emotions. Understanding the mindset of others may involve not only understanding the contents and emotions of utterances but also digging out the potential causes of emotions. The ability of models to reason the cause of emotions is crucial in many contexts—it enhances the accuracy of responses by mining the intents, reduces the possible negative emotions for the opposite, and provides more substantive emotional support. Therefore, developing a model for recognizing the causes behind emotions is crucial for a reliable dialogue system.

In recent years, significant progress has been made in conversational emotion analysis. Previous studies (Hu et al., 2023; Song et al., 2022; Zhang et al., 2023a) on Emotion Recognition in Conversation (ERC) have primarily focused on labeling emotions for individual utterances, but this study often lacks recognizing the underlying emotional stimuli present in these utterances. To address this limitation, Poria et al. (Poria et al., 2021) introduce the Causal Emotion Entailment (CEE) task, which aims to determine which specific utterances stimulate a non-neutral emotional response in the target utterance. Compared to Emotion Cause Extraction (ECE) (Lee et al., 2010; Gui et al., 2017, 2018; Fan et al., 2019) and Emotion Cause Pair Extraction (ECPE) (Xia and Ding, 2019; Hu et al., 2021b; Ding et al., 2020a; Wei et al., 2020) in discovering cause triggers in a document, identifying conversational emotion causes is challenging due to the complex conversational structure and interactions. Many works focus on understanding contextual verbal utterances (Bosselut et al., 2019; Zhao et al., 2023a; Zhou et al., 2024a), but neglecting emotions

![An example of a conversation in the RECCON-DD dataset. The arrow indicates the cause utterance for any target utterance.](page_1012_355_367_367.png)

Figure 1: An example of a conversation in the RECCON-DD dataset. The arrow indicates the cause utterance for any target utterance.
themselves can also be the cause of emotions on the counterparts beyond verbal utterances. For example, in Figure 1, speaker B’s emotion is attributed to speaker A’s happiness, which makes it difficult to reason from only verbal utterances.

To address this challenge, we turn to Emotional Contagion Theory (Hatfield et al., 1993, 2011; Liu et al., 2024), which demonstrates a process in which a person or group influences the emotions or behavior of another person through the conscious or unconscious induction of emotion states and behavioral attitudes. This means that the emotions of counterparts can elicit emotions without any linguistic cues. Generally, emotional contagion can be implicit (Tee, 2015; Wróbel and Imbir, 2019), which mainly relies on non-verbal communication (Schoenewolf, 1990), or explicit, which affects the emotions of counterparts by content (Kelly and Barsade, 2001).

Inspired by the emotional contagion process, we propose an Emotional Contagion Graph Network (ECGN) to identify emotion causes, which simulates the emotional contagion process through both explicit and implicit emotional pathways. Explicit emotional contagion is modeled through the interactions of verbal utterances called verbal cues, while implicit emotional contagion is captured through the dynamics of non-verbal emotional labels called non-verbal cues. ECGN consists of several key steps. First, ECGN extracts both non-verbal and verbal cues from the conversational context and constructs a heterogeneous conversational graph. This graph captures two types of interactions: implicit emotional contagion from non-verbal emotional labels and explicit emotional contagion from verbal utterances. Moreover, ECGN effectively transmits the dynamics within and between non-verbal and verbal cues through relational graph neural networks. Finally, a classifier predicts the emotional cause based on the integrated information.

To evaluate the proposed ECGN, we conduct extensive experiments on the RECCON-DD and RECCON-IE datasets. Results consistently demonstrate that ECGN effectively promotes the detection of causal utterances from the target utterance.

versations. Based on the granularity of the causes, it is divided into the CEE task (utterance-level causes) and the CSE task (phrase-level causes). Their approach concatenates potential causal utterances with the target utterance, but overlooks conversational interactions. To improve this, recent work has focused on contextual understanding. For instance, MuTECCEE(Bhat and Modi, 2023) employs multitask learning to model conversational context, KEC (Li et al., 2022) and KBCIN (Zhao et al., 2023a) incorporate commonsense knowledge through directed acyclic graphs, PAGE (Gu et al., 2023) leverages positional relationships, TSAM (Zhang et al., 2022) integrates attention for intra- and inter-speaker influences, and recent work (Huang et al., 2024; Zhou et al., 2024b) explores reasoning with Large Language Models (LLMs). The above works take emotion as auxiliary information accompanied by the utterances and pay attention to the verbal information, but neglect the effects of non-verbal emotional dynamics. ECGN recognizes and bridges this gap.

Emotion Recognition in Conversations. Emotion Recognition in Conversations (ERC) is a highly relevant task for CEE, which involves identifying emotion categories for the target utterance. ERC needs to predict unknown emotions in the conversation, differentiating from CEE, for which emotions are already known. Most of the present works adopt graph-based and sequence-based methods. The former (Ghosal et al., 2019; Ishiwatari et al., 2020; Hu et al., 2021c; Shen et al., 2021; Zhang et al., 2023a) builds a graph to handle interactions between utterances and speakers.

Another group of works exploits transformers and recurrent models to learn the interactions between utterances (Majumder et al., 2019; Hu et al., 2021a; Liu et al., 2022). Commonsense Knowledge is explored by KET (Zhong et al., 2019). Contrastive learning methods are also prevailing for ERC (Lewis et al., 2019; Song et al., 2022; Yu et al., 2024), which separates utterances in the representation space. Several recent works explore LLMs (Lei et al., 2023; Zhang et al., 2023b; Wu et al., 2024b) for ERC tasks. Unlike ERC methods that only rely on contextual utterances for prediction, ECGN introduces contextual emotional interactions to improve cause predictions. In addition, we discuss the connection between ECGN and the implicit emotion analysis in Appendix A.

2 Related Work

Causal Emotion Entailment  Poria et al.(Poria et al., 2021) introduced the RECCON task to identify the causes of the emotions of a speaker in con-
Emotion Cause (Pair) Extraction. Emotion cause extraction (ECE) aims to identify the causes or stimuli that trigger emotions in each sentence in a long document, which was first proposed by (Lee et al., 2010). Early studies are devoted to designing rule-based methods (Chen et al., 2010; Neviarouskaya and Aono, 2013). Recent works propose various deep networks to tackle this task (Cheng et al., 2017; Zheng et al., 2022).

The ECE task has been researched for nearly a decade, but its reliance on additional emotion annotations limits its applicability in real-world scenarios. To this end, Emotion-Cause Pair Extraction (ECPE) (Xia and Ding, 2019) is proposed to extract all pairs of emotions and corresponding causes in a document without emotion annotation. They propose a two-step framework to perform ECPE. In the following work, ECPE-2D (Ding et al., 2020a) utilizes a 2D Transformer to model clause pairs. Sequence-labeling scheme is also constructed for ECPE (Yuan et al., 2020; Cheng et al., 2021; Wu et al., 2023). Recent works have started to explore the strong reasoning and understanding abilities of LLMs for ECPE (Wu et al., 2024a; Gu et al., 2024). Unlike these two tasks, which predict emotional causes in documents, ECGN focuses on capturing the complex emotional interactions between interlocutors in real-life conversation scenarios.

3 Methodology

3.1 Problem Definition

We start by formulating the CEE task. Consider a conversation as a sequence of utterances with speakers and emotions as \( \mathbf{C} = \{(u_1, e_1, s_1), (u_2, e_2, s_2), \ldots, (u_T, e_T, s_T)\} \), where \( u_t \) is the utterance at the timestamp \( t \) in the conversation, \( e_t \in \{\text{happy}, \text{angry}, \text{sad}, \text{disgusted}, \text{fearful}, \text{surprised}, \text{neutral}\} \) is the corresponding emotion label, and \( s_t \) is the speaker identity of \( u_t \). The goal of CEE is to identify the set of utterances \( \{u_i\}(i \leq t) \) which are the emotion causes of \( u_t \) in the conversation history if \( u_t \) is a non-neutral utterance.

3.2 Model Overview

Figure 2 shows the pipeline of ECGN. It consists of several key components designed to simulate explicit and implicit emotional contagion.

The first component is to encode utterances and emotions, using a language model to extract textual representations while generating emotion representations with emotion labels.

The second component is the construction of the emotional contagion graph with the extracted representations. The emotional contagion graph contains the explicit and implicit ones. The explicit emotional contagion graph simulates the triggering of emotions by language content in the conversational context. The implicit emotional contagion graph simulates the influence of non-verbal cues on emotions in the conversational context, which are represented by emotion encodings. In this graph, the vertices represent utterances or emotions. Interactions within emotion nodes pass unconscious contagion silently, dynamics between emotion and utterances or utterances themselves actively trigger emotions. To learn the transition process, we employ relational graph neural networks and graph transformers to integrate such interactive relationships, which allows ECGN to capture causes in terms of contents and emotions.

The last component combines both the learned emotional and utterance information to construct cause representations, which are used to distinguish the causal and non-causal utterances.

3.3 Context Encoding with Emotions

Given an utterance history \( U = \{u_1, u_2, \ldots, u_T\} \) and an emotion history \( E = \{e_1, e_2, \ldots, e_T\} \), where \( T \) is the number of utterances contained in a conversation, we use a language model to extract verbal utterance representations. More specifically, we add special tokens, such as [CLS] and [SEP], which serve as markers to indicate the beginning and end of each utterance. To facilitate verbal utterance representations with emotion semantics, we construct a prompt:

\[
X_i(s_i, u_i, e_i) = s_i\ e_i\ says:\ u_i,
\]

where \( X(\cdot, \cdot, \cdot) \) transforms each utterance into an implicit emotion-rich form. For example, an utterance can be organized as John happily says: I'm so glad I bought this watch! Finally, we concatenate all the prompts in a conversation and feed them into a pretrained language model:

\[
H^t = \mathrm{PLM}([\mathrm{CLS}]X_1[\mathrm{SEP}] \ldots [\mathrm{CLS}]X_T[\mathrm{SEP}]),
\]

Where the conversational textual representations \( H^t = \mathrm{Concat}(h_1^t, h_2^t, \ldots, h_T^t) \in \mathbb{R}^{T \times d} \) is the concatenation of all last hidden states at the [CLS] token's position, \( d \) is the dimension of hidden states.
Figure 2: Overview of our proposed method. The structure of the model is shown at the bottom. First, we input the utterances and emotions into the language model to obtain the encodings of them. Then we construct a heterogeneous graph modeling the complex interaction relations, including the simulated implicit and explicit emotional contagion. Having a heterogeneous graph, we build a graph-learning model for learning dynamics between different node features. Different relations indicate that distinct information passing is needed. Finally, a cause prediction module is employed to identify the causes of emotions within the conversation.

3.4 Emotion Encoding

To leverage the non-verbal cues, we generate emotional representations at each time step with emotion labels. Given a candidate set of emotion labels \( S = \{e_1, e_2, \ldots, e_{|S|}\} \), each emotion \( e_i \) can be represented as an embedding vector:

\[
g_i = \mathrm{PLM}(e_i),
\]

Where \( g_i \in \mathbb{R}^d \), and then we concatenate emotion representations as a lookup table \( P = \mathrm{Concat}(g_1, g_2, \ldots, g_{|S|}) \in \mathbb{R}^{|S| \times d} \). These emotion representations are initialized with the original pre-trained language model. Given an emotion history \( E = \{e_1, e_2, \ldots, e_T\} \), we generate the representations for \( e_t \) through:

\[
h_t^e = \mathrm{Lookup}(P, e_t),
\]

Where \( h_t^e \in \mathbb{R}^d \) is the representation of \( e_t \), concatenating them can get the conversation emotional representation \( H^e = \mathrm{Concat}(h_1^e, h_2^e, \ldots, h_T^e) \in \mathbb{R}^{T \times d} \).

3.5 emotional contagion Graph Construction

To mimic both explicit and implicit emotional contagion processes, we construct a heterogeneous graph for each conversation history. We denote a graph as \( \mathcal{G} = (\mathcal{V}, \mathcal{E}, \mathcal{R}) \), with vertices \( v_i \in \mathcal{V} \), edges \( e_k \in \mathcal{E} \), \( r_{ij} \in \mathcal{R} \) is the type of relation between \( v_i \) and \( v_j \).
Our graph \( \mathcal{G} \) contains two types of nodes:

Utterance node: We consider the ith utterance in the conversation as a node \( v_i^t \in \mathcal{V}^t \), whose representations are initialized with its utterance-level features \( h_i^{t,(1)} = h_i^t \) for any time step \( i \).

Emotion node: We treat each emotion in the conversation as a node \( v_i^e \in \mathcal{V}^e \) and initialize the representations with \( h_i^{e,(1)} = h_i^e \).

Then the set of nodes can be represented as:

\[
\mathcal{V} = \mathcal{V}^t \cup \mathcal{V}^e,
\]

where utterance node \( \mathcal{V}^t = \{u_i\} \), emotion node \( \mathcal{V}^e = \{e_i\} \) and \( i \in [1, T] \).

Our graph \( \mathcal{G} \) contains three types of edges:

Emotion-Emotion edge: To simulate the non-verbal implicit emotional contagion, we connect the current utterance \( i \) with a past context window size of \( p \) and a future context window size of \( f \). We believe that adjacent utterances of utterance \( i \) have the most significant impact. For the sake of message passing between utterances, each utterance vertex has an edge with the timestamp \( i \) utterance of the past: \( v_{i-p}^e, v_{i-p+1}^e, \ldots, v_{i-1}^e \), the future utterances: \( v_{i+1}^e, v_{i+2}^e, \ldots, v_{i+f}^e \) and \( v_i^e \) itself. These edges are denoted as \( \mathcal{E}^{ee} = \{(e_i, e_j), (e_j, e_i)\} \), where \( \max(0, i - p) \leq j \leq \min(i + f, T) \), and \( i \in [1, T] \). \( \mathcal{E}^{ee} \) enables the non-verbal emotional information to transmit intra- and inter-speakers.

Utterance-Utterance edge: Verbal communications can elicit emotions, we connect utterance nodes to construct explicit emotional contagion
graph to capture the conscious emotions as \( \mathcal{E}^{ee} = \{ (u_i, u_j), (u_j, u_i) \} \), which allows the utterances themselves to cause the emotions.

Utterance-Emotion edge: To further establish the interactions between emotions and utterances, we connect the utterance node \( i \) with its emotion node to model the interaction within a speaker. The edges can be represented as \( \mathcal{E}^{ue} = \{ (u_i, e_i), (e_i, u_i) \} \), which connects the mutual effect of emotion and utterance within a speaker. Besides, multi-hop message passing enables such an effect to spread across speakers.

Then the set of edges can be represented as:
\[
\mathcal{E} = \mathcal{E}^{uu} \cup \mathcal{E}^{ue} \cup \mathcal{E}^{ee},
\]
where \( \mathcal{E} \) includes non-verbal implicit emotional dynamics \( \mathcal{E}^{ee} \), and verbal explicit emotional dynamics \( \mathcal{E}^{ue} \) and \( \mathcal{E}^{uu} \).

3.6 Emotional Dynamic Interaction
To effectively pass the information between nodes and learn the dynamics, we utilize R-GCN (Schlichtkrull et al., 2018), which can integrate different relationships between vertices and learn the node representations:
\[
h_{i}^{*,(l+1)} = \sigma \left( \sum_{r \in \mathcal{R}} \sum_{j \in \mathcal{N}_i^r} \frac{1}{|\mathcal{N}_i^r|} W_r^l h_{j}^{*,(l)} + W_0 h_{i}^{*,(l)} \right),
\]
Where \( \mathcal{N}_i^r \) is the set of neighboring nodes of node \( i \) under the relationship \( r \), \( h_{i}^{*,l} \) is the representations for node \( i \) which is either emotional or textual node after layer \( l \in [1, L] \). \( W_r \in \mathbb{R}^{d_1 \times d_2} \) and \( W_0 \in \mathbb{R}^{d_1 \times d_2} \) are learnable parameters to transform the neighborhood information within relationship \( r \). R-GCN layers not only transmit the emotional dynamics within the emotional nodes and utterance nodes but also capture the interactions between emotions and utterances. Then, the node representations are mapped to a shared representation space. To step further, We exploit GraphTransformer (Shi et al., 2020) to learn rich utterance representations. More specifically, the representations can be calculated as follows:
\[
h_{i}^{*,(l+1)} = W_1 h_{i}^{*,(l)} + \sum_{j \in \mathcal{N}_i} \alpha_{i,j} W_2 h_{j}^{*,(l)},
\]
\[
\alpha_{i,j} = \text{Softmax} \left( \frac{(W_3 h_{i}^{*,(l)}) (W_4 h_{j}^{*,(l)})}{\sqrt{d}} \right),
\]
where the \( \alpha_{i,j} \) is the attention coefficient and \( d \) is the hidden size. The final utterance representation is then obtained by concatenating the emotional and utterance node representations at layer \( L \):
\[
h_i = \text{Concat}(h_i^{*,(L)}, h_i^{e,(L)}),
\]

3.7 Cause Prediction
To predict the cause of the target utterance, we obtain the cause representation \( c_t \) by concatenating the utterance representations between the target utterance \( T \) and the historical utterance \( i \):
\[
c_i = \text{ReLU}(W_5[h_i; h_t] + b_1),
\]
\[
\hat{y}_i = \text{Sigmoid}(W_6 c_i + b_2),
\]
Where \( \hat{y}_i \) is the probability of utterance \( i \) is the cause of emotion in the target utterance. \( W_5 \in \mathbb{R}^{d_2 \times d_3}, W_6 \in \mathbb{R}^{d_3 \times 1} \) \( b_1 \in \mathbb{R}^{d_3}, b_2 \in \mathbb{R} \) are learnable parameters. The cross-entropy loss function is adopted for optimization.

4 Experimental settings

4.1 Dataset and evaluation metrics

Dataset. We conduct experiments on two benchmark datasets, RECCON-DD and RECCON-IE (Poria et al., 2021), derived from DailyDialog (Li et al., 2017) and IEMOCAP (Busso et al., 2008), respectively. RECCON-DD serves for training and in-distribution testing, while RECCON-IE is used as an out-of-distribution test set to evaluate the generalization ability of models. The statistics of the RECCON-DD dataset are shown in Table 3. The data samples used for the experiments were constructed by pairing each non-neutral emotional utterance with its historical utterances, including itself, one by one. If a historical utterance was found to be the cause of an emotional utterance, the utterance pair was labeled as positive; otherwise, the pair was labeled as negative. In addition, we analyze the distribution of cause pairs in the conversations, as shown in Figure 3, about 80 % of the emotion causes are located within two time steps before the target utterances, indicating the high impact of neighboring emotions and utterances. We further discuss the locality of the emotion causes in Appendix B.

Metrics. Following previous work (Poria et al., 2021), we adopt the macro-averaged F1 score to evaluate performance. Also, the F1 score for positive and negative samples is reported.
<table>
  <tr>
    <th>Methods</th>
    <th>Neg. F1 (%)</th>
    <th>Pos. F1 (%)</th>
    <th>Macro F1 (%)</th>
  </tr>
  <tr>
    <th colspan="4">ECE Methods</th>
  </tr>
  <tr>
    <td>KAG (Yan et al., 2021)</td>
    <td>86.35</td>
    <td>58.18</td>
    <td>72.26</td>
  </tr>
  <tr>
    <td>Adapted (Turcan et al., 2021)</td>
    <td>88.18</td>
    <td>64.53</td>
    <td>76.36</td>
  </tr>
  <tr>
    <th colspan="4">ECPE Methods</th>
  </tr>
  <tr>
    <td>ECPE-2D* (Ding et al., 2020a)</td>
    <td><b>94.96</b></td>
    <td>55.50</td>
    <td>75.23</td>
  </tr>
  <tr>
    <td>ECPE-MLL* (Ding et al., 2020b)</td>
    <td>94.68</td>
    <td>48.48</td>
    <td>71.59</td>
  </tr>
  <tr>
    <td>RankCP* (Wei et al., 2020)</td>
    <td><b>97.30</b></td>
    <td>33.00</td>
    <td>65.15</td>
  </tr>
  <tr>
    <td>HCL-ECPE (Hu et al., 2024)</td>
    <td>88.52</td>
    <td>66.47</td>
    <td>76.93</td>
  </tr>
  <tr>
    <th colspan="4">CEE Methods</th>
  </tr>
  <tr>
    <td>ChatGPT 0-shot† (Zhao et al., 2023b)</td>
    <td>85.25</td>
    <td>51.33</td>
    <td>68.29</td>
  </tr>
  <tr>
    <td>ChatGPT 1-shot† (Zhao et al., 2023b)</td>
    <td>82.10</td>
    <td>52.84</td>
    <td>67.47</td>
  </tr>
  <tr>
    <td>MuTEC<sub>CEE</sub> (Bhat and Modi, 2023)</td>
    <td>83.46</td>
    <td>61.62</td>
    <td>72.54</td>
  </tr>
  <tr>
    <td>PAGE (Gu et al., 2023)</td>
    <td>89.42</td>
    <td>65.20</td>
    <td>77.02</td>
  </tr>
  <tr>
    <td>KEC † (Li et al., 2022)</td>
    <td>88.85</td>
    <td>66.55</td>
    <td>77.70</td>
  </tr>
  <tr>
    <td>KBCIN † (Zhao et al., 2023a)</td>
    <td>89.65</td>
    <td>68.59</td>
    <td>79.12</td>
  </tr>
  <tr>
    <td>TSAM (Zhang et al., 2022)</td>
    <td>89.75</td>
    <td>68.59</td>
    <td>79.17</td>
  </tr>
  <tr>
    <td>DAM (Kong et al., 2023)</td>
    <td>89.35</td>
    <td>69.32</td>
    <td>79.34</td>
  </tr>
  <tr>
    <th>ECGN(ours)</th>
    <th>90.57<sup>±0.19</sup></th>
    <th><b>69.78<sup>±0.54</sup></b></th>
    <th><b>80.17<sup>±0.24</sup></b></th>
  </tr>
</table>

Table 1: Results on the RECCON-DD dataset. †and ♦ denotes the results obtained from (Zhao et al., 2023b) and (Poria et al., 2021).* represents our method is significant statistically (p-value < 0.05).

<table>
  <tr>
    <th>Methods</th>
    <th>Neg. F1 (%)</th>
    <th>Pos. F1 (%)</th>
    <th>Macro F1 (%)</th>
  </tr>
  <tr>
    <td>ECPE-2D (Turcan et al., 2021)</td>
    <td><b>97.39</b></td>
    <td>28.67</td>
    <td>63.03</td>
  </tr>
  <tr>
    <td>ECPE-MLL (Yan et al., 2021)</td>
    <td>93.55</td>
    <td>20.23</td>
    <td>57.65</td>
  </tr>
  <tr>
    <td>RankCP (Wei et al., 2020)</td>
    <td>92.24</td>
    <td>15.12</td>
    <td>54.75</td>
  </tr>
  <tr>
    <td>KEC (Li et al., 2022)</td>
    <td>86.08</td>
    <td>19.72</td>
    <td>52.90</td>
  </tr>
  <tr>
    <th>ECGN(ours)</th>
    <th>93.55<sup>±0.24</sup></th>
    <th><b>42.99<sup>±1.76</sup></b></th>
    <th><b>68.26<sup>±0.91</sup></b></th>
  </tr>
</table>

Table 2: Results on the RECCON-IE dataset.

<table>
  <tr>
    <th>RECCON-DD</th>
    <th>Train</th>
    <th>Dev</th>
    <th>Test</th>
  </tr>
  <tr>
    <td>Positive Pairs</td>
    <td>7026</td>
    <td>328</td>
    <td>1767</td>
  </tr>
  <tr>
    <td>Negative Pairs</td>
    <td>20558</td>
    <td>838</td>
    <td>5296</td>
  </tr>
  <tr>
    <td>Number of Dialogues</td>
    <td>834</td>
    <td>47</td>
    <td>225</td>
  </tr>
</table>

Table 3: Statistics of the RECCON-DD dataset.

4.2 Baselines

For a comprehensive evaluation, we compare our method with the following baselines:

(1) ECE and ECPE methods: KAG (Yan et al., 2021) that alleviates positional bias problem and improves the semantic dependencies using CSK; Adapted (Turcan et al., 2021) jointly detects emotion and emotion cause enhanced by CSK; ECPE-2D (Ding et al., 2020a) uses the 2D representation to simulate emotion-cause pairs interactions with a 2D transformer; ECPE-MLL (Ding et al., 2020b) extends ECPE-2D by incorporating multi-label learning to extract emotion cause. RankCP (Wei et al., 2020) emphasizes inter-clauses modeling with a ranking perspective for ECPE; HCL-ECPE (Hu et al., 2024) introduces hierarchical contrastive learning for ECPE.

(2) CEE methods: KEC (Li et al., 2022) injects commonsense knowledge for a directed acyclic graph; KBCIN (Zhao et al., 2023a) leverages event-centered commonsense knowledge (Bosselut et al., 2019) to capture the inter-utterance relationships; PAGE (Gu et al., 2023): A position-aware graph-based model distinguishes different speakers for causal entailment. MuTEC<sub>CEE</sub> (Bhat and Modi, 2023) exploits multitask learning to extract conversational emotions, emotion causes, and entailment. TSAM (Zhang et al., 2022) proposes a two-stream attention model to separately model emotions and speakers. In-Context-Learning (Zhao et al., 2023b): uses ChatGPT (GPT-3.5-turbo-0301)
Figure 3: Distribution of the distance between positive pairs. The distance denotes the temporal difference between the causal and target utterances. The blue part indicates the portion that distance is less than 2.

with few-shot demonstrations to test CEE performance.

4.3 Implementation Details

We use Roberta-base (Liu et al., 2019) as the pre-trained language model for a fair comparison. All experiments are conducted using the PyTorch (Paszke et al., 2019) and Torch Geometric (Fey and Lenssen, 2019) frameworks, with results reported across five repetitions.

5 Main Results and Analysis

Results on RECCON-DD. Table 1 shows the performance comparison of ECGN with state-of-the-art methods. It is observed that the ECE and ECPE methods perform worse than the CEE methods. For example, Adapted (Turcan et al., 2021) serves as the best method among ECE and ECPE methods that can achieve 76.36% macro F1 score, which performs mediocrily among the CEE methods. ECGN surpasses 3.81%, indicating the effectiveness of our design for CEE. ECPE and ECE models fail to leverage available emotion labels of utterances and model utterance and emotion interactions in conversation structure, leading to their poorer performance.

Compared to CEE methods, we outperform TSAM by 1% overall and KBCIN, which incorporates external knowledge. The second-best baseline DAM incorporates discourse parsing to enhance long-distance cause classification. However, as shown in Figure 3, most causal relations occur in the local context, highlighting the effectiveness of our simulation of emotional contagion in the local context to improve the overall performance. In addition, ECGN has an overwhelming performance advantage over ChatGPT; the possible reason is that ChatGPT is not well aligned with the complex data annotation for CEE. The experimental results are significantly better than the baselines under the t-test, which validates the robustness of ECGN.

Results on RECCON-IE. Table 2 shows the experimental results on RECCON-IE. ECGN demonstrate improvements compared to other baseline methods. In terms of Macro-F1 score, ECGN outperforms the best-performing baseline by 4.12%, highlighting its superior OOD generalization capability.

<table>
  <tr>
    <th>Emotion</th>
    <th>Graph</th>
    <th>Neg. F1</th>
    <th>Pos. F1</th>
    <th>Macro F1</th>
  </tr>
  <tr>
    <td>✓</td>
    <td>✓</td>
    <td><b>90.57</b></td>
    <td><b>69.78</b></td>
    <td><b>80.17</b></td>
  </tr>
  <tr>
    <td>✓</td>
    <td>✗</td>
    <td>89.98</td>
    <td>68.48</td>
    <td>79.23</td>
  </tr>
  <tr>
    <td>✗</td>
    <td>✓</td>
    <td>89.77</td>
    <td>68.45</td>
    <td>79.11</td>
  </tr>
  <tr>
    <td>✗</td>
    <td>✗</td>
    <td>89.38</td>
    <td>68.29</td>
    <td>79.04</td>
  </tr>
</table>

Table 4: Effects of different components on the performance of the proposed ECGN model.

5.1 Ablation Study

We conducted a series of ablation studies on ECGN. The results, as depicted in Table 4, highlight the criticality of each element in our approach. Removing the graph structure and concatenating the emotional representations with utterance representations, as well as removing the implicit emotional graph part, harms the performance to a large extent. This result demonstrates the effectiveness of ECGN in dealing with emotional causes happening in the local time with the mutual influence of emotions. Removing \( e_i \) in \( X_i \) decreases macro F1 by 0.3%, indicating the importance of the influence of the emotional state on the features of the utterance. Ablation results for edges are provided in Appendix C.

5.2 Effect of the Number of R-GCN Layers

We conducted an investigation into the influence of the number of layers in the Relational-GCN architecture. The findings, as presented in Table 5, indicate that the incorporation of more global information with the deeper graph networks introduces confused context since most causes are adjacent to the target utterances. Besides, the use of deep graph neural networks resulted in performance degradation due to oversmoothing, as reported in previous studies (Kipf and Welling, 2016). Experiments show that employing two layers of Relational-GCN
<table>
  <tr>
    <th>Layers</th>
    <th>Neg. F1</th>
    <th>Pos. F1</th>
    <th>Macro F1</th>
  </tr>
  <tr>
    <td>1</td>
    <td>90.22</td>
    <td>68.27</td>
    <td>79.24</td>
  </tr>
  <tr>
    <td>2</td>
    <td><b>90.57</b></td>
    <td><b>69.78</b></td>
    <td><b>80.17</b></td>
  </tr>
  <tr>
    <td>3</td>
    <td>90.38</td>
    <td>68.67</td>
    <td>79.52</td>
  </tr>
  <tr>
    <td>4</td>
    <td>90.11</td>
    <td>68.70</td>
    <td>79.40</td>
  </tr>
  <tr>
    <td>5</td>
    <td>90.43</td>
    <td>68.15</td>
    <td>79.29</td>
  </tr>
  <tr>
    <td>6</td>
    <td>90.46</td>
    <td>69.02</td>
    <td>79.74</td>
  </tr>
</table>

Table 5: The performance of using a different number of R-GCN layers under the window size 2.

<table>
  <tr>
    <th>Model</th>
    <th>Neg. F1</th>
    <th>Pos. F1</th>
    <th>Macro F1</th>
  </tr>
  <tr>
    <td>R-GCN</td>
    <td>90.57</td>
    <td><b>69.78</b></td>
    <td><b>80.17</b></td>
  </tr>
  <tr>
    <td>R-GAT</td>
    <td>90.56</td>
    <td>69.45</td>
    <td>80.04</td>
  </tr>
  <tr>
    <td>FiLMConv</td>
    <td><b>90.75</b></td>
    <td>69.50</td>
    <td>80.12</td>
  </tr>
</table>

Table 6: The performance of using different graph networks as a message passing mechanism.

proved to be a balanced approach. The decrease in performance may be due to redundant information introduced by the excessive number of layers.

5.3 Effect of Contextual Window Size

We also report the performance under a large range of window sizes. In Figure 4, the trend of performance shows that it first increases with the increase of window size. The increase in window size should have a larger perception field to aggregate more information; however, the intrinsic property of a conversation decides that a non-neutral emotion is more likely to be triggered by the neighbor’s utterances, and distant utterances may introduce irrelevant information (Ding et al., 2019).

5.4 Performance Across Different Message Passing Mechanisms

We investigated whether the performance of ECGN depends heavily on the specific message passing mechanism by substituting the default R-GCN with two alternatives: R-GAT (Busbridge et al., 2019) and FiLM (Brockschmidt, 2020). The results presented in Table 6 reveal stable performance in these different variants of graph neural networks. This suggests that ECGN is robust and works effectively with various underlying message-passing structures.

5.5 Efficiency of ECGN

To demonstrate the efficiency of ECGN, we compared its parameter count and average inference time per utterance for recognizing emotional causes against the base RoBERTa-base model. Inference time was measured on a single NVIDIA L40S GPU. The results shown in Table 7 indicate that incorporating graph layers adds only 4.16% more parameters and increases inference time by just 9.09%. These minimal computational overheads highlight the high parameter and computational efficiency of ECGN, confirming its scalability.

<table>
  <tr>
    <th>Model</th>
    <th># Parameters (M)</th>
    <th>Runtime (s)</th>
  </tr>
  <tr>
    <td>Roberta-base</td>
    <td>124.64</td>
    <td>0.022</td>
  </tr>
  <tr>
    <td>ECGN</td>
    <td>129.83</td>
    <td>0.024</td>
  </tr>
</table>

Table 7: ECGN increases minimal computation costs and extra parameters compared to the base model.

![Test results under different window sizes on the RECCON-DD dataset. The red dashed line depicts the trend generated by polynomial fitting.](page_1012_1012_388_282.png)

Figure 4: Test results under different window sizes on the RECCON-DD dataset. The red dashed line depicts the trend generated by polynomial fitting.

5.6 Can Implicit Emotional Dynamics Identify Causes?

To verify how much the causes depend on the implicit emotion dynamics, we remove utterance nodes and only retain emotion nodes to determine the causes. As reported in Figure 5, the performance works almost equal to ChatGPT 1-shot and slightly better than RankCP on the Macro F1 score. It indicates that implicit non-verbal emotional dynamics play a critical role in causal emotion entailment, as demonstrated by its performance even in the absence of explicit utterance-level information.

6 Case Study

We exhibit a case study in Table 8. In this case, the speaker \( S_A \) first feels sad when finding his chicken tastes dry, which elicits a sad emotion. Subsequently, speaker \( S_B \) turns his emotion from neutral to sad not only because speaker \( S_B \)'s tastes are dry, but also is influenced by \( S_A \). As shown in Table 8, removing the implicit emotional contagion network enables the model to understand only the utterance
<table>
  <tr>
    <th>Turn</th>
    <th>Speaker</th>
    <th>Utterance</th>
    <th>Emotion</th>
    <th>w/IEC</th>
    <th>w/o IEC</th>
    <th>Label</th>
  </tr>
  <tr>
    <td>1</td>
    <td>A</td>
    <td>Hey George, how is your chicken?</td>
    <td>Neutral</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
  </tr>
  <tr>
    <td>2</td>
    <td>B</td>
    <td>My chicken tastes all right, but it is pretty dry. How is your fish?</td>
    <td>Neutral</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
  </tr>
  <tr>
    <td>3</td>
    <td>A</td>
    <td>My fish is pretty dry too.</td>
    <td>Sad</td>
    <td>[3]</td>
    <td>[3]</td>
    <td>[3]</td>
  </tr>
  <tr>
    <td>4</td>
    <td>B</td>
    <td>It’s almost as if this food has been sitting a little too long. It doesn’t seem fresh.</td>
    <td>Sad</td>
    <td>[3, 4]</td>
    <td>[4]</td>
    <td>[3, 4]</td>
  </tr>
</table>

Table 8: Case study of a conversation instance shows that non-verbal implicit emotional dynamics (IEC) enables the model to rectify incorrect cause predictions.

![Bar chart comparing F1 metrics for Neg F1, Pos F1, and Macro F1 between RankCP, ChatGPT 1-shot, and Ours (implicit)](page_357_629_377_246.png)

Figure 5: Performance comparison with baselines using only non-verbal implicit emotional dynamics.

semantics, which overlooks speaker \( S_A \)'s sadness as an emotion trigger of \( S_B \), leading to mistaken classification. Besides, by analyzing our predicted emotion causes, we find that the following aspects mainly cause prediction errors: First, the causal relationship happens when the distance between the target utterance and the cause utterance is large. This type of error presents a challenge to trace back to distant previous dialog history. The second category is sudden emotional change, which confuses the model about causal relations. Solving these two kinds of errors needs a more fine-grained reasoning process to understand the mental state, e.g. Theory of Mind (Ma et al., 2023; Jin et al., 2024; Strachan et al., 2024; Yu et al., 2025) because conversational context is simple, which is unable to provide sufficient information to accurately identify those causes, and external memory to retrieve relevant information from long conversation history (Zhong et al., 2024; Maharana et al., 2024) also have potential capturing distant information.

7 Conclusion

In this paper, we introduce the Emotional Contagion Graph Network (ECGN) as an innovative model that improves causal emotion entailment in conversations by simulating the impact of non-verbal implicit emotions on the counterpart’s emotions. By drawing inspiration from the Emotional Contagion Theory, the model constructs a heterogeneous conversational graph to capture explicit and implicit emotional dynamics between speakers, simulating the influence between emotions themselves and interactions with utterances in a conversation to determine the causes. Extensive experimentation on the benchmark datasets demonstrates superior performance of ECGN over state-of-the-art baselines. Ablation studies and evaluations further validate the robustness and effectiveness of our approach, as well as the importance of implicit emotional dynamics in the conversation for causal emotion entailment.

Acknowledgement

We would like to thank the anonymous reviewers for their valuable feedback. This work is supported by the NSFC (No. 62206126, 62376120) and the Fundamental Research Funds for the Central Universities (No. 022114380016).

Limitations

Our method only uses text-based information to simulate the emotional contagion. We recognize that multimodal information, such as video and audio can enhance the non-verbal emotional contagion, including facial expressions, body language, posture, tone of voice, and other non-verbal cues. Such an integration would better reflect the dynamics of emotional contagion in the real world, leaving a large room for future benchmarks and the development of new methods.

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Changzhi Zhou, Dandan Song, Zhijing Wu, Linmei Hu, Jing Xu, and Yanru Zhou. 2024a. A multi-level multi-task progressive framework based on relational graph convolutional networks for causal emotion entailment. Knowledge-Based Systems, page 112108.

Deyu Zhou, Jianan Wang, Linhai Zhang, and Yulan He. 2021. Implicit sentiment analysis with event-centered text representation. In Proceedings of the 2021 conference on empirical methods in natural language processing, pages 6884–6893.

Zhihan Zhou, Xue Gu, Yujie Zhao, and Hao Xu. 2024b. Pop-cee: Position-oriented prompt-tuning model for causal emotion entailment. In Findings of the Association for Computational Linguistics ACL 2024, pages 4199–4210.

A Connection to Implicit Emotion Analysis

Our work is relevant to implicit emotion analysis, which often focuses on recognizing emotions from text where the specific emotion is not explicitly stated (Zhou et al., 2021; Liao et al., 2024). emotional contagion is a widely existing psychological phenomenon, which is about people spreading emotions unconsciously to influence or change others’ emotions. Our proposed ECGN recognizes it and explicitly models it to improve the recognition of emotional causes in conversations.

B Local Context Justification

ECGN focuses on the local conversational context, which is motivated by the observation that emotional causes frequently appear near the target utterance. This characteristic exists in the RECCON-DD dataset, where high inter-annotator agreement (kappa score: 0.7928) indicates reliable annotations, reflecting the characteristic of cause pairs being concentrated in a local context in real-world scenarios.

The reliance on local context is further verified in a document-level emotion cause extraction dataset (Gui et al., 2018; Ding et al., 2019), collecting data from SINA City News, which is also a real-world scenario. Statistics show that more than 95% of emotion causes happen at a relative distance of less than 2. This finding demonstrates that emotional causes often happen in a local context and verifies the rationality of our modeling of the local conversational context.

<table>
  <tr>
    <th>Model</th>
    <th>Neg. F1</th>
    <th>Pos. F1</th>
    <th>Macro F1</th>
  </tr>
  <tr>
    <td>ECGN</td>
    <td><b>90.57</b></td>
    <td><b>69.78</b></td>
    <td><b>80.17</b></td>
  </tr>
  <tr>
    <td>- Utterance-Utterance Edges</td>
    <td>90.29</td>
    <td>67.97</td>
    <td>79.13</td>
  </tr>
  <tr>
    <td>- Emotion-Emotion Edges</td>
    <td>89.84</td>
    <td>68.57</td>
    <td>79.20</td>
  </tr>
  <tr>
    <td>- Utterance-Emotion Edges</td>
    <td>90.12</td>
    <td>68.45</td>
    <td>79.29</td>
  </tr>
</table>

Table 9: The performance of removing each type of edges.

C Additional Ablation Study

To verify the effectiveness of emotional dynamic interaction, we conduct ablation studies by removing emotion-emotion edges, utterance-utterance edges, and utterance-emotion edges. The results shown in Table 9 indicate that removing each type of edge will lead to a significant drop in performance, as removing them hinders the information passing in the graph, demonstrating that the edges are effective in passing emotional contagion and contributing to performance.

Reviewer paper summaries: This paper deals with CEE/ERC by recognizing not only the verbal cues in dialogue but also non-verbal emotional influences. Motivated by the observation that emotions can be elicited through both explicit language and implicit emotional contagion, the authors propose the ECGN, which constructs a heterogeneous graph where nodes represent utterances and emotion labels, and edges capture both explicit verbal interactions and implicit non-verbal emotional dynamics. By applying relational graph neural networks, the method effectively models how emotions propagate and trigger one another in conversation. Empirical results demonstrate that ECGN outperforms existing approaches, confirming that incorporating implicit emotional cues significantly enhances causal emotion entailment.
Summary of strengths: 1.	The manuscript is clearly organized, making it straightforward to follow and understand the core concepts presented.
2.	I like the Psychological/Cognitive-based motivation, which aligns with my personal belief that understanding how emotions arise is essential for predicting their causes.
3.	The results presented are interesting, and the analysis is thorough, offering valuable insights.
Summary of weaknesses: 1.	**Related Works Discussion:** Given the recent advances in graph-based CEE/ERC models, could the authors elaborate a bit more on what differentiates their ECGN from these existing methods? What specific theoretical foundations from Emotion Contagion Theory (Hatfield et al., 1993) support these modeling/structural choices?
2.	**Representation of Non-verbal Cues:** Your model simulates implicit emotional contagion using emotion labels to capture non-verbal cues. Although there is a risk of oversimplification, Mehrabian’s theory is often cited to suggest that in face-to-face communication, approximately 55% of the message is conveyed through body language, 38% through vocal tone, and only 7% through the actual words use. How do you justify that these textual proxies accurately reflect non-verbal emotional dynamics, and what are the limitations compared to approaches that incorporate actual multimodal signals (e.g., as discussed in Wu et al., 2024b)?
3.	**Regarding the integration of non-verbal cues and implicit emotional contagion:** In your ECGN architecture, you introduce nodes representing emotion labels themselves. Could you clarify how you distinguish and quantify implicit emotional influence (i.e., “emotions themselves spreading”) versus explicit verbal triggers in your final representation? Zhao et al. (2023) demonstrate that a misalignment between textual cues and inferred emotions can lead to false positives—how do you mitigate or correct for such mismatch in your approach?
4.	**Handling of multi-cause or overlapping causes:** Emotions in conversation can be triggered by multiple prior utterances or a combination of textual and contextual factors. Does ECGN handle the scenario where multiple utterances collectively cause a single emotion (Gu et al., 2023)? If multiple nodes are identified as likely causes, how do you rank or combine them to achieve a final decision, and what strategies have you explored to handle multi-cause entailment?

**Reference:** \
Hatfield et al. “Emotional contagion”. Current directions in psychological science’93. \
Wu et al. “Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances.” ArXiv’24. \
Zhao et al. “Knowledge-Bridged Causal Interaction Network for Causal Emotion Entailment.” AAAI’23. \
Gu et al. “PAGE: A Position-Aware Graph-Based Model for Emotion Cause Entailment in Conversation.” ICASSP’23. \
Comments, suggestions and typos: No further comments.
Overall assessment scores: 3.5