CMCF-SRNet: A Cross-Modality Context Fusion and Semantic Refinement Network for Emotion Recognition in Conversation
Year: 2023

Abstract:
Emotion recognition in conversation (ERC) has attracted enormous attention for its applications in empathetic dialogue systems. However, most previous researches simply concatenate multimodal representations, leading to an accumulation of redundant information and a limited context interaction between modalities. Furthermore, they only consider simple contextual features ignoring semantic clues, resulting in an insufficient capture of the semantic coherence and consistency in conversations. To address these limitations, we propose a cross-modality context fusion and semantic refinement network (CMCF-SRNet). Specifically, we first design a cross-modal locality-constrained transformer to explore the multimodal interaction. Second, we investigate a graph-based semantic refinement transformer, which solves the limitation of insufficient semantic relationship information between utterances. Extensive experiments on two public benchmark datasets show the effectiveness of our proposed method compared with other state-of-the-art methods, indicating its potential application in emotion recognition. Our model is available at https: //github.com/zxiaohen/CMCF-SRNet.

heading: 1 Introduction
text: Emotion recognition in conversation (ERC) plays an important role in affective dialogue systems, aiming to understand and generate empathetic responses (Raamkumar and Yang, 2022). Most studies on ERC focus primarily on the textual modality (Majumder et al., 2019). Although they can be easily extended to multimodal paradigms by performing early or late fusion (Poria et al., 2017a), it is difficult to capture contextual interactions between modalities, which limits the utilization of multiple modalities. For instance, a tensor fusion network based on the utterance-level explicit alignment learning both intra-modality and intermodality interactions via the Cartesian product was
∗Corresponding author
firstly created (Zadeh et al., 2017), then a low-rank multimodal fusion network to improve efficiency and reduce trainable parameters was designed (Liu et al., 2018). A conversational memory network aligns features from different modalities by fusing multi-view information (Hazarika et al., 2018b). In addition, a cross-modal transformer was integrated that learns attention between two-modal features, thus enabling implicit enhancement of the target modality (Tsai et al., 2019). A multimodal fusion graph convolutional network for ERC was put forward discussing the impact of fusion methods of various modalities (Hu et al., 2021).
However, these methods mostly use a simple concatenation ignoring complex interactions between modalities, resulting in leveraging context information insufficiently or the problem of data sparseness. Besides, they simply consider the emotional impact of context in the whole conversation but neglect the emotional inertia of speakers and the fact that the local context may have a higher impact than long-distance utterances.
As both the current utterance and the surrounding contexts are vital for the emotion perception, previous works proposed different methods including RNN-based models and graph-based models to explore contextual clues: A LSTM-based
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model (Poria et al., 2017b) and an interactive conversational memory network ICON (Hazarika et al., 2018a) capture interaction and history context, while DialogueRNN model (Majumder et al., 2019) leverages distinct GRUs to capture speakers’ contextual information.
Other popular approaches use graph-based neural networks and solve the context propagation issues in RNN-based architectures, including DialogueGCN (Ghosal et al., 2019) which first constructed the graph considering both speaker and conversation sequential information. Recent approaches like DAG-ERC (Shen et al., 2021) combined the advantages of conventional graph-based models and RNN-based models, a semantics GAT was employed to adjust the weight of knowledge (Tu et al., 2022), latent correlations have been leveraged among the utterances through a multibranch graph network (Ren et al., 2022). Meanwhile, existing GNN models use different aggregation schemes for a node to aggregate feature messages from its neighbors (Yuan et al., 2022): Graph convolutional networks use mean pooling while graph attention networks aggregate neighborhood information with trainable attention weights to capture local details (Isufi et al., 2022). Furthermore, graph networks consider global graph information during aggregation and have been used to explore the semantic relationship between regional objects and global concepts (Zhu et al., 2022).
However, the existing graph-based methods also have limitations. First, they mostly ignore the semantic similarity between context utterances leading to a lack of semantic correlation. Second, these models learn node embeddings by capturing local network structure but ignore the position of the node within a broader context of the graph structure and the deep semantic features from a global view. To address this issue, we investigate a semantic graph-based transformer.
In this work, we propose a cross-modality context fusion and semantic refinement network (CMCF-SRNet). First, we investigate a crossmodality context fusion module to integrate textual and audio information considering the impact of the local context and the emotional inertia of speakers, achieved by a cross-modal locality-constrained attention. Second, we design a semantic refinement module to extract effective semantic features and contextual information including the nearby surroundings and distant information. The main
contributions can be summarized as follows:
• Our proposed CMCF-SRNet is developed firstly by exploring cross-modal locality-constrained transformer to facilitate multimodal context fusion, bridging the gap of current works on ERC. • We define a semantic graph to model the relations between neighboring utterances and a semantic graph-based transformer encoder is adopted to capture the global underlying semantic. • We systemically analyze the importance of each component, including cross-modal transformerbased fusion, and semantic refinement methods. Experimental results demonstrate the performance of our proposed model.

heading: 2 Methodology
text: Given a dialogue D = {u1, u2, ..., uN}, where N denotes the number of utterances and um is the mth utterance in the conversation, the emotion recognition in conversation task aims to predict the emotion label for each utterance in the conversation. Each utterance involves two sources of data corresponding to acoustic (a) and textual (t) modalities represented as um = {uam, utm} where u(a)m ∈ Rda for audio and u(t)m ∈ Rdt for text, where da, dt represent feature dimensions. The combined input features matrix for all utterances in a dialogue is given by: Xi = [u (i) 1 , u (i) 2 , ...u (i) n ] where i ∈ {a, t}.
The overall architecture of our proposed CMCFSRNet is outlined in Fig. 2 and summarized as follows: (1) The acoustic/textual feature matrix for utterances is first fed to acoustic/linguistic embedding block to obtain unimodal representations, and then cross-modal locality-constrained attention (LCA) is utilized to generate high-level crossmodal features which go into an attentive selection block; (2) We define a semantic graph and employ the relational graph convolutional network to capture the inter-utterance dependence, then leverage an aggregation of effective semantic features by integrating a semantic-position encoding; (3) The nodes embeddings are fed into the classifier to obtain the final prediction. In the following three subsections, we discuss in detail the specific implementation of the proposed innovation modules.

heading: 2.1 Cross-modality Context Fusion Module
text: We consider the order of the utterances by adding the triangle positional embedding (PE) directly to Xi while i ∈ {a, t}, and we define the query Q(h)i ,
the key K(h)i and the value V (h) i vector for encoding input features Xi ∈ Rn×di as shown in Fig. 3 (a). An attention map of attention weights for a single attention head α(h) ∈ Rn×n is obtained by the attention mechanism and is used to compute a weighted sum of the values and obtain the output.
O (h) i = softmax(
Q (h) i (K (h) i ) T
√ k
)V (h) i (1)
Ôi (h) = [O (1) i ⊕O (2) i ⊕ · · · ⊕O (N) i ]W (2) where W ∈ RkN×di , N represents the total number of heads. Finally, we add a residual connection followed by a layer norm and obtain X ′i.
After an intra-modal transformer to capture the global temporal dependencies of unimodal features, we apply a cross-modal locality-constrained transformer to capture the local contextual information focusing on correspondences between different modalities. We extend the traditional transformer to a two-stream cross-modal transformer to model interactions between two modalities, where each cross-modal transformer block is combined with a cross-modal locality-constrained attention layer. The attention layer could combine the information from the different sources of data to transform the text features using the feature map of audio. Querys, Keys, and Values has been defined as Q(h)i = X ′ iWh,q, K (h) j = X ′ jWh,k, V (h) j =
X ′jWh,v for a single attention head where i, j ∈ {a, t}, i ̸= j. Considering that to predict the emotion of an utterance, the speaker’s recently stated utterance has the greatest correlation with its emotion, thus, we propose a locality-constrained and speaker aware attention LCA (Fig. 3 (b)) by masking the traditional weight map Wij = softmax(QiKTj ) in Eq. (3) as in Eq. (4).
Oij(Q,K) = Wij · Vj (3) Oij(Q,K) = (Wij ⊙ LCA) · Vj (4)
We design intra-speaker masks SA to focus on the utterances of the current speaker and model the emotional inertia of this interlocutor’s emotional flow on the current utterance:
SAm,n = { 1 if sm = sn; 0 otherwise.
(5)
where sm, sn are respectively the speakers of utterances um and un. As the emotion of current utterance is more affected by the local utterances close to it, a common idea is to apply a fixed window, but in order to solve the problem that the fixed-window method treats utterances in the window equally, we calculate the relative position weighting RP of hm and hn, then feed into a sigmoid function. Finally, we apply an element-wise product to obtain LCA = sigmoid(RP )×SA, which combines both local context and speaker information.
RPm,n = { M− C(n−m)2 if m,n ≤ N ; 0 otherwise.
(6) where N is the actual length of the dialogue, and both M and C are hyperparameters. Here, we set M and C to 5 and 1.5 respectively.
To obtain the fusion representation combing both intra-modal and cross-modal contextual information from two modalities, an attentive selection block is proposed to distribute different importance to different modalities, we propose a model-
level fusion strategy instead of a simple concatenation (Chen and Jin, 2016). Experimental results in Section V verify the effectiveness.
We extract utterance-level acoustic features h (a) m ∈ Rd, text features h(t)m ∈ Rd, cross-modal features h(c)m ∈ Rd for each utterance um (where um is the mth utterance in the conversation). Then we equalize feature dimensions of all inputs and concatenate them together considering different contributions of different modalities to focus on important modalities. Technically, at a given time, given the input feature H = [H(1), H(2), ...,H(K)] with K the number of modalities. The score for each modality is computed by:
ai = ReLU(W TH(i) + b) (7)
αi = exp(ai)∑K j=1 exp(aj)
(8)
the attention scores αatt ∈ R1×K where K = 3. The final multimodal features gm ∈ Rd2 are generated as follows with the output X ′c ∈ Rn×d2 :
g(j) = concat([α1H (1), ..., αKH (K)]) (9)

heading: 2.2 Semantic Refinement Module
text: To explore the semantic relationships between utterances in a dialogue, a novel model for semantic information refining is proposed, which is illustrated in the semantic refinement module in Fig. 2. It mainly consists of two stages: relational semantic graph construction and semantic information refinement. The well-defined semantic graph is fed into a two-layer RGCN to compute semantic features of utterances and their interaction relations. Then the global semantic information is further extracted by a semantic graph-transformer. Semantic Graph Construction: To establish semantic relations between the nearby utterances and to capture both inter-speaker and intra-speaker effects, we define a semantic graph Gs= (Vs,Es) based on the conversational semantic-aware dependency. Each utterance is represented by a node and different connection edges represent directed relations (past and future), Vs denotes a set of utterance nodes, and Es ⊂ Vs × Vs is a set of relations that represent the semantic similarity between the utterances, defined as Eq. (10).
simi,j = 1− arccos( gTi gj
∥gi∥∥gj∥ ) (10)
We define intra-relations between the utterances spoken by the same speaker Rintra ∈{ USi → USi } and inter-relations by different
speakers, Rinter ∈ { USi → USj } i ̸=j . We further consider a context window using P and F as hyperparameters to denote relations between the past P utterances and future F utterances for every utterance. The relational semantic graph can be regarded as a local-view modeling of the relationships between utterances in a dialogue and covering semantics features.
Semantic Information Refinement: In this paper, a modified relational graph convolution layer is adopted to capture local dependency defined by the relations. The node representations and edge weights are feed into a two-layer correlation-based RGCN which can be summarized as follows, here, we introduce the concept of aggregate functions to generalize the above mechanism:
h (1) i =σ(
∑
r∈R
∑
j∈Nri
ai,j qi,r W (1)r gj + ai,iW (1) 0 gi)
h (2) i = σ(
∑
j∈Nri
W (2)h (1) j + ai,iW (2) 0 gi) (11)
where N ri denotes the neighboring indices of each node under relation r ∈ R, W (m)i are learnable parameters, σ(·) is the activation function as ReLU. In this way, each graph convolution layer models the interaction between utterances, and refines the semantic features.
Then, we adopt a semantic graph-transformer to extract global semantic information from the node feature taking in consideration the relative position of utterances (Fig. 4). It adopts the vanilla multihead attention into graph learning by taking into account nodes connected via edges. Given node features H = [h1, h2, ..., hn] obtained from RGCN, we define two encodings to represent semantic relationship between two nodes in a graph. The first is relative position encoding P , each vector of P represents the topological relation represented by their shortest path distance between two nodes, the second is semantic encoding S defined by Eq. (10), we take an addition operation and obtain SP .
aij = (Wqhi) T (Wkhj)√ d value +Φsemij (12) Φsemij = qiSPϕsemij + kjSPϕsemij (13) h′i = N∑
i=1
âij(vj + SPϕsemij ) (14)
Previous methods focus on encoding graph information into either the attention map or input features. First, our method encodes positional and semantic information represented by edge weight
into attention map to take the global context structure into consideration. Moreover, it encodes the hidden features of value as shown in Eq. (14).

heading: 2.3 Emotion Classifier
text: The output of graph transformer is fed into a MLP with fully connected layers and get the prediction values of the utterance ui under each emotion label:
hi = ReLU(W1h ′ i + b1) (15) Pi = softmax(W2hi + b2) (16) ŷi = argmax(Pi) (17)
where ŷi is the emotion label predicted for the utterance ui. We choose the categorical cross-entropy loss function during training as is shown below:
L = − 1∑N i=1 Li
N∑
n=1
C∑
i=1
yi · logŷi (18)
where N is the number of conversations and Li is the number of utterances in the ith conversation.

heading: 3 Experiments and Results

heading: 3.1 Datasets
text: In this section, we conduct several experiments to evaluate our proposed method and compare it with state-of-the-art baselines on two benchmark datasets, the dataset statistics are given in Table 1:
- IEMOCAP (Busso et al., 2008) dataset contains approximately 12 hours of dyadic emotional improvised and scripted conversations (10039 utterances). The labelling of each utterance was determined by 3 annotators as the following categorical labels: anger, happiness, sadness, neutral, excitement, frustration, fear, surprise. To compare with state-of-the-art frameworks, we adopt their dataset settings respectively the first four categories for the 4-way condition (Lian et al.,
2021) and the first six categories for the 6-way conditions. Following previous works, utterances from the first 8 speakers are used as the training and validation sets while the others are used as the testing set. - MELD (Poria et al., 2019) is a large-scale multi-party conversational dataset which contains 13708 utterances and 1433 conversations from TV series Friends, and each utterance is annotated with one of the following labels: anger, joy, sadness, neutral, disgust, fear and surprise.

heading: 3.2 Implementation Details and Metrics
text: We performed all experiments on the Pytorch deep learning framework with the Intel Core i7-12700H and the NVIDIA RTX3060 GPU. The software environment includes Python 3.9, Pytorch 1.12.1, and CUDA 11.3. Adam optimizer with an initial learning rate of 0.0001 is used to optimize the parameters in the proposed CMCF-SRNet and a dropout rate of 0.5 is adopted. The head number is set to 4 for cross-modal transformer and 2 for graph-transformer. Besides, audio features (size 100) are extracted using OpenSmile (Eyben et al., 2010) and text features (size 768) are extracted using sBERT (Reimers and Gurevych, 2019). We re-run on each dataset five times and calculate the mean and standard deviations.
We evaluate the performance of emotion recognition using the following as evaluation metrics: WAA is a weighted average accuracy over different emotion classes with weights proportional to the number of utterances in a class. WF1 is a weighted mean F1 over different emotion categories with weights proportional to the number of utterances in a particular class.
WAA = ∑C j=1Nj ∗Accuracyj∑C
j=1Nj (19)
WF1 = ∑C j=1Nj ∗ F1j∑C
j=1Nj (20)

heading: 3.3 Overall Performance
text: For comparison, we implement following state-ofthe-art baseline approaches to evaluate the performance of our proposed method: BC-LSTM (Poria et al., 2017c) uses a bidirectional LSTM to encode contextual information, but ignoring the speaker-specific information. DialogueGCN (Ghosal et al., 2019) is the first to model a conversation by a graph, transforms
the emotion classification into a graph-based node classification problem. MMGCN (Hu et al., 2021) uses multimodal dependencies and speaker information effectively and applies GCN to obtain contextual information. CTNet (Lian et al., 2021) utilizes transformer to obtain the multimodal rerpesentation by modeling the intra-modal and cross-modal interactions. A-DMN (Xing et al., 2022) models self and interspeaker influences and then synthesizes this two factors to update the memory. I-GCN (Nie et al., 2022) utilize the graph structure to represent conversation at different times and apply the incremental graph structure to imitate the process of dynamic conversation. MMDFN (Hu et al., 2022) proposes a graph model where both speaker dependency of the interlocutors is leveraged and latent correlations are captured.
To verify the effectiveness of our proposed method, we compare our proposed CMCF-SRNet with state-of-the-art baseline approaches on the IEMOCAP (4-way), IEMOCAP (6-way) and MELD datasets on the overall performance and for each emotion category. As is shown in Table 2, our model outperforms all the baselines mentioned above on the two datasets. For the IEMOCAP (4- way) dataset, ours achieves the new state-of-the-art record, 86.5% on F1 and 86.9% on WAA, which shows an absolute improvement of 2.0% on F1 score. For the IEMOCAP (6-way) dataset, our proposed method also succeeds with 70.5% on WAA and 69.6% on F1 which outperforms Bc-LSTM and DialogueGCN by 10.7% on WAA, 10.6% on WF1 and 5.3% on WAA, 5.5% on WF1 possibly due to the cross-modal context fusion architecture applied in our proposed model; in addition, it outperforms CTNet and MMDFN which utilize multimodal fusion approaches by 2.3%~2.5% on WAA and 1.5%~2.1% on WF1, the reason lies in that these methods focus on the multimodal represen-
tation ignoring the semantic relationship between utterances. Our proposed CMCF-SRNet also outperforms I-GCN which highlights the semantic correlation information of utterances without considering multimodal fusion approach.
In addition, we present classification accuracies and F1 scores for each emotion category and visualize the confusion matrices of the testing set in Fig. 5. For the IEMOCAP (6-way) dataset, the improvements on classification performance can be seen for most emotion categories over existing approaches (Table 2). Specifically, we notice an improvement of F1-score for happy, neutral, angry and excited emotions which show the improved ability of the model to identify relevant emotions. Meanwhile, we find neutral and anger emotions can be confused with the frustration emotion (Fig. 5 (a)) as the majority of the utterances are labeled as the frustration. Also, the happiness emotion can be confused with the excitement emotion (Fig. 5 (a)) due to our similar perception of these emotions.

heading: 4 Discussion

heading: 4.1 Effect of Cross-modality Context Fusion
text: First, we conduct uni-modal experiments using text modality and our proposed method still gives comparable performance compared to the SOTA unimodal architectures (Table 3). As shown in Table 4, adding more information via other modalities helps to improve the performance.
To verify the effectiveness of our cross-modal locality constrained transformer-based contextual fusion strategy, we conduct the ablation experiments as listed in Table 4: 1) Without cross-modal Locality Constrained Attention (w/o LCA): We remove the transformers and combine utterance-level features directly with Attentive Selection Block; 2) Without Attentive Selection Block (w/o ASB); 3) Ours: Our proposed method. The results demonstrate that our proposed CMCF-SRNet with LCA significantly improved the WF1 and WA indexes. After adding LCA, the WA and WF1 of the model were improved by 3.2% and 3.4% respectively, indicating that the cross-modal transformer can comprehensively improve the performance. Then, as is shown in Fig. 6, we take the lexical modality for example and visualize its attention weights in conversations after different components. The red rectangles at the first line indicate that the 10th and the 14th utterances in the conversation show more importance for the emotion detection according to the intra-modal transforme while that in the second line indicates that according to the crossmodal transformer the 4th to 7th utterances should be paid more attention. These results verify that the outputs of cross-modal transformer contribute to conversational emotion recognition.
To verify the effectiveness of our attentive selec-
tion block (ASB), we implement three comparison methods. Experimental results (Table 4) demonstrate that our ASB achieves the best performance. It shows an absolute improvement over Add on WA by 2.5% on IEMOCAP, probably because Add copes with the multimodal features equally and cannot highlight emotion-relevant modalities while ASB can prioritize important modalities via the attention mechanism. Meanwhile, it also shows an improvement over Concatenate and Tensor Fusion which may suffer from the curse of dimensionality by 1.2% and 3.2% as our proposed method can generate more effective smaller-size multimodal features for emotion recognition.

heading: 4.2 Effect of Semantic Refinement
text: To observe the effect of the graph-based semantic refinement components, we visualize the features with and without the semantic refinement components (Fig. 8). We easily notice a better formation of emotion clusters proving the necessity of capturing semantic dependency in utterances. Additionally, we conduct ablation experiments on the correlation-based RGCN and Semantic GraphTransformer respectively, specifically, we respectively remove the semantic edge weight (SEW) in the RGCN and semantic-positional encoding (SPE) in the Graph-Transformer, after removing the SEW, WA and WF1 on IEMOCAP decreased by 2.6%, 1.9% respectively, while after removing the SPE, WA and WF1 on IEMOCAP decreased by 3.2%, 2.7% respectively, which indicates that the proposed semantic encoder is necessary, the result in Table 4 shows the advantages of focusing on emotional semantic clues.

heading: 4.3 Visualization and Interpretability
text: Given the importance of interpretability in machine learning, we investigate the necessity of local context realised by cross-modal LCA and global semantic context captured by semantic refinement module. We explore the distribution of distances between the target utterance and its second (2nd) highest attended utterance according to our attention scores for all the utterances correctly classified. First, most of the correctly classified utterances depend on their local context when a significant portion is also present for the distant context. Besides, the dependence on distant context shows more significance for the 2nd highest attention, which highlight the importance of the long-term emotional dependency. Meanwhile, the contextual dependence exists both towards the past and the future utterances.
Moreover, we conduct experiments with multiple window sizes as presented in Fig. 10. The window size can be modified during the training period. A larger window size would result in better performance for cases where the inter and intra speaker dependencies are maintained for longer sequences. In contrast, a smaller window size would be better where the topic frequently changes in dialogues and speakers are less affected by another speaker. These results support our design combining the locality-constrained attention and semantic refinement from a global-view.

heading: 5 Conclusion
text: In this paper, we propose a novel framework for multimodal emotion recognition which contains two innovative modules: The cross-modal locality-constrained context fusion leverages the transformer-based method to focus on localness, effectively improved the multimodal interaction. The semantic refinement module makes full use of the semantic relation information from a global view. Experiments on two public datasets and the results demonstrate that our proposed CMCF-SRNet is superior to the existing state-of-the-art methods. The ablation experiments prove the effectiveness of the two innovative modules. The detailed discussion shows that our proposed CMCF-SRNet has satisfactory generalization ability and interpretability, indicating that it has the potential for practical use for emotion recognition.
Limitation
Although experiments on two public datasets show the effectiveness of our proposed method compared with other state-of-the-art methods, we notice that our proposed model fails to distinguish similar emotions effectively going through the prediction results, as frustrated and anger, happy and excited (Fig. 5(a)). Moreover, our proposed model tends to misclassify samples of other emotions to neutral on MELD due to the majority proportion of neutral samples in these datasets. We will address this issue in future work by integrating a component for capturing the fine-grained emotions.

heading: Acknowledgements
text: We appreciate the insightful suggestions from the anonymous reviewers to further improve our paper. This work was supported in part by the National Natural Science Foundation of China under Grant 62201023 and Beijing Natural Science Foundation under Grant Z220017, and in part by the Beijing Municipal Education Commission-Natural Science Foundation [KZ202110025036].

heading: A For every submission:

heading: 3 A1. Did you describe the limitations of your work?
text: Section IV

heading: 3 A2. Did you discuss any potential risks of your work?
text: Section IV

heading: 3 A3. Do the abstract and introduction summarize the paper’s main claims?
text: Section I
7 A4. Have you used AI writing assistants when working on this paper? Left blank.
B 7 Did you use or create scientific artifacts? Left blank.
B1. Did you cite the creators of artifacts you used? No response.
B2. Did you discuss the license or terms for use and / or distribution of any artifacts? No response.
B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? No response.
B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? No response.
B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? No response.
B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. No response.
C 3 Did you run computational experiments? Section III

heading: 3 C1. Did you report the number of parameters in the models used, the total computational budget
text: (e.g., GPU hours), and computing infrastructure used? Section III
The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance.

heading: 3 C2. Did you discuss the experimental setup, including hyperparameter search and best-found
text: hyperparameter values? Section III

heading: 3 C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary
text: statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section IV

heading: 3 C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did
text: you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section III
D 7 Did you use human annotators (e.g., crowdworkers) or research with human participants? Left blank.
D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response.
D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants’ demographic (e.g., country of residence)? No response.
D3. Did you discuss whether and how consent was obtained from people whose data you’re using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response.
D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response.
D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

heading: Future Work
text: We will address this issue in future work by integrating a component for capturing the fine-grained emotions