E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation
Authors: Fengyi Fu
Lei Zhang
Quan Wang
Zhendong Mao
Venue: EMNLP 2023 Main (2023)
Keywords: empathetic dialogue generation
graph network
emotion perception
natural language generation
TL;DR: We propose a novel framework comprehensively covering the learning, utilizing, and supervising of emotion correlation, which explicitly mine and incorporate this intrinsic emotion correlation to enhance empathetic perception and generation.

Abstract: Achieving empathy is a crucial step toward humanized dialogue systems. Current approaches for empathetic dialogue generation mainly perceive an emotional label to generate an empathetic response conditioned on it, which simply treat emotions independently, but ignore the intrinsic emotion correlation in dialogues, resulting in inaccurate emotion perception and unsuitable response generation. In this paper, we propose a novel emotion correlation enhanced empathetic dialogue generation framework, which comprehensively realizes emotion correlation learning, utilization, and supervising. Specifically, a multi-resolution emotion graph is devised to capture context-based emotion interactions from different resolutions, further modeling emotion correlation. Then we propose an emotion correlation enhanced decoder, with a novel correlation-aware aggregation and soft/hard strategy, respectively improving the emotion perception and response generation. Experimental results on the benchmark dataset demonstrate the superiority of our model in both empathetic perception and expression.

E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation
Fengyi Fu', Lei Zhang': Quan Wang", Zhendong Mao
"University of Science and Technology of China "Beijing University of Posts and Telecommunications ff1 #f142536f@mail wst..uttooooo edu. cn, leizh23@ustc. edu. cn wangquan@bupt. edu. cn, zdmao@ustc. edu. cn
Abstract
last year but am very glad be alive to today.
felt terrified when was hit drunk driver
afrald
Achieving empathy is a crucial step toward hu- manized dialogue systems. Current approaches for empathetic dialogue generation mainly per- ceive an emotional label to generate an empa- thetic response conditioned on it, which simply treat emotions independently, but ignore the intrinsic emotion correlation in dialogues, re- sulting in inaccurate emotion perception and unsuitable response generation. In this paper, we propose a novel emotion correlation en- hanced empathetic dialogue generation frame- work, which comprehensively realizes emotion correlation learning, utilization, and supervis- ing. Specifically, multi-resolution emotion graph is devised to capture context-basse emo- tion interactions from different resolutions, fur- ther modeling emotion correlation. Then propose an emotion correlation enhanced de- coder, with novel correlation-awar aggre- gation and soft/hard strategy, respectively im- proving the emotion perception and response generation. Experimental results the bench- mark dataset demonstrate the superiority of our model in both empathetic perception and ex- pression.
Speaker
0.36
afraid
0.36
Main-emution
grataful
0.20
grateful
Perception
coaciion
currdutiion
turrfiied
Contest ombbodding
Contadd
www.:oeddddddaeeeeeee. grangful
ng
Stage2
Reapuase
Regpoore: I'te sorry to
Regoote: OA I'n to sorry Anar that. flur rhol's good rlst sou tsol thees in.
Generation
boar that.
Exsiting
Figure 1: A real empathetic dialogue generation case based on our method (right) and existing methods (left), which is divided into two stages: (a) main-emotion perception, (b) response generation. For more detailed visualization of (a) refer to Fig.6.
Most existing methods follow multi-task learn- ing paradigm, jointly training emotional classifi- cation task and dialogue generation task to achieve response generation with empathetic constraints. Recent works take their effort on two aspects. The first focuses on improving the emotion perception, for example, by introducing external knowledge (Li et al., 2020b, 2022b; Sabour et al., 2022), min- ing emotion causes (Kim et al., 2021; Gao et al.. 2021), more fine-grained emotion modeling (Li et al., 2020a; Kim al., 2022). The other focuses on promoting the generation strategy, based on mix- ture of experts (Lin et al., 2019), different emotion look-ahead reward functions (Shin et al., 2020). emotional mimicry (Majumder et al., 2020) and so on. In general, these methods first perform main- emotion prediction with a single-label emotional classifier, then inject the predicted emotion into generation to achieve empathetic expression.
1 Introduction
Empathy is a desirable human trait that improves the emotional perceptivity in emotion-bonding so- cial activities, helping to achieve a humanized di- alogue system (Smith, 2006; Singer and Lamm, 2009). Empathetic dialogue generation (EmpDG) which aims at perceiving the emotional expressions in dialogue to generate appropriate responses rich empathy, is proposed and has attracted extensive attention with its ability to improve user experience and satisfaction in multiple domains (Fitzpatrick et al., 2017; Wang et al., 2021).
"Corresponding author. This work is supported by the Na- tional Key Research and Development Program of China under Grant NNo.0221 YFF09016000 the National Science Fund for Excellent Young Scholars under Grant (No.62222212) 12), and the National Natural Science Foundation of China under Grant
The above paradigm implicitly introduces an in- dependent assumption on different emotions, both in modeling and utilization, which respectively from the learning for maximizing separation be-
(N6.61876223).

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three secondary emotions
eration framework, namly E-CORE, with three tai- lored modules to address above challenges, Specif- ically, we propose a novel directed weighted graph. which captures the subtle emotion interactions context from different resolutions, further encod- ing the intrinsic emotion correlation. Then we design an emotion correlation enhanced decoder, which adopts correlation-awe -warr aggregation and soft/hard strategy, incorporating the correlated occurrence emotions to improve emotion percep- tion and response generation, respectively. Mean- while, an emotion correlation loss constructed to provide multi-emotion emotion regular constraints.
58.05% secandary
two secondary emotions
1.. 99%
Tour and more secondary emotions only main-cmation
Figure 2: Statistics of secondary emotions proportion in the EMPATHETICDIALOGUU dataset samples.
tween different emotions in classification and the abandonment of secondary emotions in generation. However, studies on social psychology (Vanstee- landt et al., 2005; Martinent et al., 2012) suggest that human emotions are not completely indepen- dent, but with an intrinsic correlation, manifested dialogues and responses are typically accom- panied with the co-occurrence of multiple emo- tions (Martinent al., 2012). This independent assumption with ignoring the emotion correlation directly impairs main-emotion perception, as the main-emotion as a whole feature should be co- determined by the occurred emotions in context. (Fig. 1-a. the common correlation weights of grate- ful and terrified helps distinguish the true emotions afraid). Moreover, this assumption is also harmful for response generation, as the model dominated by one emotion lacks the ability to recognize emo- tional transitions (Fig.1, a transition from afraid for accident to grateful for survival), resulting in unsuitable responses lb, only "sorry to hear" for survival). Therefore, considering the emotion correlation is necessary for precise emotion percep- tion and better empathetic expression. Statistical result" on benchmark dataset in Fig.2, which is calculated based on the quantity of other-emotion- related words in samples, further suggests that the emotion correlation learning is significant for Em- PDG task with a proportion of samples containing secondary emotions reaches 84.04%.
Our contributions are summarized as follows: 1) We propose breaking the emotion independence sumption existing in current methods and modeling the intrinsic emotion correlation. To the best of our knowledge, this is one of the first frameworks in EmpDG that explicitly models and utilizes emo- tion correlation to enhance emotion perception and response generation. 2) We propose a distinctive method with three tailored modules respectively ad- dressing the emotion correlation learning, utilizing, and supervising, which effectively and accurately capture the correlated co-occurrence emotions in dialogues even under single-label, enhancing empa- thy perception and expression. 3) Extensive experi- ments verify the superiority of our method on both emotion prediction (8.34% % in accuracy) and re- sponse generation (8.53% % in perplexity). Ablation studies and specialized experiments on constructed multi-emotion annotated sub-dataset also validates the fidelity of our emotion correlation learning.
Related Work
2.1 Emotional Dialogue Generation
In recent years, open-domain dialogue systems have achieved great progress (Li et al., 2016; Liu et al., 2016; Zhong et al., 2019; Zhang et al., 2020a; Shen et al., 2021: I; Zhu et al., 2022). As the combi- nation of emotion and personality leads to more human-like system, the emotional dialogue gen- eration task which aims to generate emotional re- sponses according to specified emotion label, was proposed and developed (Song et al., 2019; Dong et al., 2021; Ide and Kawahara, 2021; Liang et al., 2021; Li al., 2021; Tu et al., 2022). Some works (Firdaus et al., 2021) also make efforts in multi-emotion guided generation, however, these works based on manually annotated emotions, fo- cus on the encoding for provided multi-emotion.
As the annotation for all subtle emotions in di- alogues is hard and inefficiency, we propose to mine and incorporate this intrinsic emotion corre- lation into single-labeled EmpDG. There are three challenges: 1) modeling and learning the multi- emotion correlation; 2) utilizing the correlated co-oceurrence emotions without biasing toward to the labeled emotion; 3) providing supervision to avoid excessive or erroneous introduction of multi- emotion information.
To this end, we propose novel Emotion CORrelation Enhanced empathetic dialogue gen-
'Specific details for statistics are supplied in appendix A.

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Renutions
Emotion Coméxtion
Resqunee
Weighn Mebrix
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Modifed
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Transfomer Deoader
Embbed
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Ganert
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6:11
Context
Scenified
Sample-spactie (!(E**)
Enetion Cornivsion Lozs
Improved Graph Updating
(C3SS)
tadlyy
Inttal emotion
Imversant
edge weighta
emotion
Contest Encoding
seight
rearting
removing
Emotion Signal Perceptron
Soft strategy
Hand stratogy
drank
drusk
Sott/tied Gateed Generator
(Sottma Paeoo
OTSL, Parc- KJ
diver
Erictin /Word nodes Comaltted connection
K-meokaionn Graph
Bospun
100
Emotion Coriialion
wewsowd conneciion
Tranaformer
perception
overall frumework
(b) vuft/hard gated generatur
Figure 3: (a). The overview of the proposed E-CORR, which consists of three phases. 1) context encoding: encoding the dialogue context and all emotions into embedding features and contextual representation; 2) multi-resolution emotion graph network: capturing the context-based emotion interaction from different resolutions to encode the emotion correlation; 3) emotion correlation enhanced decoding: incorporating the emotion correlation to enhance emotion signal perception and response generation. (b). The design of soft/hard gated generator used phase 3.
Our work more simulates real dialogue scenarios, imitating the listener's perception and inference for context emotions, which focuses the multi- emotion learning with emotion correlation.
expression.
Proposed Approach
Given dialogue context U [u,, U2, thm] of m utterances, empathetic dialogue generation aims to generate the next empathetic response y with emotional consistency and informative expres- sion. Optionally, the task performs emotion pre- diction based on context semantic understanding to achieve empathetic constraints. In this section, give detailed introduction to our proposed E- E-CORE, which explicitly mines and incorporates the emotion correlation enhance empathetic per- ception and expression. The framework consists of phases: context encoding, multi-resolution emotion graph network, and emotion correlation enhanced decoding, as is illustrated in Fig.3.
2.2 Empathetic Dialogue Generation
Unlike emotional dialogue generation, the empa- thetic dialogue generation task aims at generating empathetic responses, based on perceived emotions instead of definite annotated emotions. Rashkin et al. (2019) first proposed the task and contributed new task benchmark and large-scale empathy dialogue dataset. Then several works (Majumder et al., 2020; Li et al., 2020a; Kim et al., 2021; Gao et al., 2021) make efforts in enhancing em- pathy perception. Lin et al. (2019) proposed a multi-decoder model combining the emotional re- sponses of the appropriate listeners, as every lis- tener is independent. Kim et al. (2022) proposed feature transition recognizer for identifying feature shifts between utterances, enhancing semantic un- derstanding. Li al. (2022b); Sabour et al. (2022) introduced commonsense knowledge to improve situation understanding. Li et al. (2022a) further proposed serial encoding and amotion-knowledse interaction method which effectively utilized fine- grained emotion features and commonsense knowl- edge to enhance empathy response. However, these works mostly rely on single-emotion prediction capture empathy signals, ignoring the emotion co-occurrence existing in dialogues. In this work, we investigate the correlation baased emotion co- occurrence to enhance empathetic perception and
3.1 Context Encoding
Following previous methods (Sabour al., 2022: Li al., 2022b), we first concatenate the dia- logue context into a long word sequence and insert a special [CLS] token at the start, i.e., X [CLS, 31, T2, HM 11], where M - is the to- tal number of words in U and indicates [CLS]] Then we represent the context embedding as syn- thesis of three kinds of embeddings: word embed- ding, position embedding (Vaswani al., 2017) and dialog state embedding, as the dialog state in- dicates each word comes from the speaker or the listener. The context embedding x is fed into a transformer encoding layer (Vaswani et al., 2017)

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obtain the contextual representation:
Considering the symmetry of emotion correlation.
more generally, we adopt re-parameterization trick to replace the direct training for R by repre- senting R as the mnner-produce of the re- re-parameter matrix S E IR PxPx i.,, R = SUS, where the diago- nal values are always set to 1.
x = ew ew(X) + ep (x) + ea(X),
(1)
hx- = EnCerans (x)
hx R Mx and D is the feature dimension.
Logically, we define the initial edge weights for each node as the normalization for its correspond- ing neighboring nodes:
3.2 Multi-resolution Emotion Graph Network Inspired by the methods in social psychology stud- ies (Vansteelandt et al., 2005; Scherer, 2013) which explore the emotion correlated co-occurrence through emotion words interaction, we construct a multi-resolution emotion graph based on the word emotion intensities, to capture the mntexttbased emotion interaction from different resolutions, for further emotion correlation learning.
Cj s//max((c), for v; by E V.: 1/P, for us E Vas Uj E Ve Softmax, for 22 E V.
(3)
Additionally, all nodes are connected to [CLS] node with weight for context interaction. The initial features h for word nodes Vn, and emotion nodes V. are defined as the word embeddings x and emotion embeddings ew (V=) (Eq.1).
Similar to Li et al. (2022b), we construct the emotion intensity annotation from SKEP (Tian et al., 2020), serving as the bridge for emotion graph modeling. As SKEP outputs a [O,1] score n(x;) identifying the positive degree of word x; (0.5 means neutral), the emotion intensity of each word is defined G (n(x);) 0.5)², and c [CI....,CMM
Graph Updating. We design a novel multi- resolution attention mechanism that effectively real- izes the independent updating and layer-out fusion of graph features for different resolutions, without increasing complexity. Specifically, the nodes and edges features of layer on k-th graph are updated:
the emotion intensity for all context words.
K JENY A. (//)))
Specially, the multi- resolution emotion graph is composed of two kinds of nodes, i.e., .M word nodes Ve for M context words (including (CT:S) and P emotion nodes Ve for P emotions; and two kinds of edges, i.e., inter- acted connections for word nodes, and correlated connection for emotion nodes.
Graph Construction.
b/+1 IT'
11
(4)
EX E (w n²) @ (W +
+1/h),
(5)
A/A Ai = Softmax JENY (A ALA A/
where,
(6)
((Q)"h)) "K
(7)
where K is the resolution level, vik O K.L (D/K)xD whe E IIIDD W Rixl are learn- able matrixes. A indicates the calculated atten- tion score of node to neighboring node j on the k-th graph and i-th layer. @ denotes element-wise multiplication and IT' is a MLP network. || de- notes concatenation for each graph. This design smoothly promotes the multi-head attention into multi-resolutie updating, where node features and edge weights are independently updated with corre- sponding connection in each resolution (head), then node features are fused layer-by-layer for global feature sharing.
For word nodes, the emotion graph is required to capture the subtle emotions interaction existing in the context for correlation learning. Starting from the global interaction, as different emotional transitions will lead to different response emotions, we innovate a basic interacted connection, i.e., word node connects to previous word nodes and all emotion nodes. Further, capture more direct emotion interactions, as the emotion intensity e pre- liminary indicates the word emotional importance, by setting different thresholds and screening out relatively unimportant word nodes, the basic graph will be extended to refined interacted graphs that attend to emotional information at melti-resolltion. For emotion nodes, the emotion graph is required model the intrinsic emotion correlation, thus we construct the emotion correlated connection, i.e. edges from emotion nodes each other, combined with a global learning matrix R E PxP, simply yet effectively encoding the correlation weights.
After several rounds of graph updating and a sum process for K-graph edge weights, obtain the representation of emotion graph: word-to-emotion edge weights Ew E MxPP emotion-to-emoote edge weights Eeee C PxP and word node features hnode E IR MxD used for subsequent emotion per- ception and response generation.

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3.3 Emotion Correlation Enhanced Decoding
emotional information:
With the sample-specific emotion correlation cap- tured by graph, we detail the utilization of the correlation-basee emotion co-occurrence, to en- hance the emotion signal perception and empa- thetic response generation, respectively.
hemo = o (Woh emo) O b + h hemo
(12)
where We weight metric and hemo E R indi- cates the final attention features to P emotions.
With the final emotion attention, soft and hard strategies are proposed respectively, to improve the graph for an effective utilization of correlated co- occurrence emotions. A straightforward way is soft strategy, which treats the attention features as an emotional soft label, serving the new initial edge weight for emotion nodes:
Emotion Signal Perceptron.
We adopt correlation-awer -wwrr aggregation to enhance emotion perception. Specifically, as the edge weights of graph intuitively reflect the attention to emotions, we define the global perception signal:
h2 Emo
E = (Softmax(Demo))) for Uj E Ve.
(13)
This processing refers to Fig. I-a, where the column- summation for En fuses the attention weights of M words to each emotion. E is initialized by R and updated with sample context, equivalent the sample-specins emotion correlation weights with diagonal values reset to This design smoothly achieve an attention correlated aggregation for the co-occurrence emotions in the context.
However, the soft strategy may introduces re- dundant emotional information, resulting noise interference. Therefore, we further propose another hard strategy to directly screen emotions. As the context- iwwpootiiiieeeeaa//eeeeaaaa emotions reflect a great distinction in attention features, we divide emotions into irrelevant and relevant categories, based the principle of maximizing the variance between the two categories, also known as the OTSU algorithm (Otsu, 1979) (details in appendix B):
Then the global perception signal is combined with contextual representation hx (Eq.2), followed by a linear layer and a softmax layer to obtain the emotion category distribution. Specifically:
Vreicvant virreicvant = OTSUUuueurtt Ve). (14)
h emn Whb g Wahx),
(9)
By removing the nodes and connected edges of ir- relevant emotions, hard strategy helps realize com- prehensive attention to important emotions.
P(e| X) Softmax 2 mm));
(10)
Lemo log(P(e - X).
(11)
In summary, the soft strategy is more flexible while the hard is more stable, both of which suc- cessfully achieve adaptive selection and utiliza- tion of co-occurrence emotions.
hx RDD is the mean pooling feature of hx, We Px2P and Wz PxD are weight matrixs of linear layers. hgmo the obtained main perception signal. Our model minimize the cross-entropy loss between the predicted main-emotion e and ground truth emotion ex for optimization.
Finally, after carrying out soft or hard strategy to improve the emotion graph to focus on signifi- cant emotions, we obtain the improved graph fea- tures through another forward process, based on the parameters//eiight-shhte improved graph net- work. As node features reserve not only emotional information, but also fed the improved node features hnde into the modified transformer decoder (de- tails in appendix C) for generation:
Soft/Hard Gated Generator. Main-emotion sig- nal perception provides annotated emotion supervi- sion. but may also suppress other emotions, impair- ing subsequent generation. Thus, we design both soft and hard gated strategies to capture the mean- ingful oo-occurrence emotions, combined with the emotion graph to pay more attention to meaning- ful emotions, and further achieve co-occurrence emotions guided generation.
semantic
information,
$; Dec (y hx, haode); (15) P(y Yat X) Softmax s.s)),
Specifically, avoid the supervised suppression may be caused by direct use of hm gated atten- tion mechanism is adopted to extract meaningful emotion features from the global and main emo- tion perception signals, which both contain rich
(16)
where Yet = [No. Ut 1] is the masked response and hx is the contextual representation. As most di- alogue generation tasks, the negative ligelikeli

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loss is used the optimization objective:
4.2 Baselines
We conduct experiments to compare our E-CORE with the following state-of-the-art baselines: 1) Transformer (Vaswani et al., 2017): a transformer- based model for response generation. 2) MIME (Majumder et al., 2020): a model connsidering polarity-based emotion clusters and emotional mimicry. 3) EmpDG (Li et al., 2020a): a model
Lgnn
log P(v Yea, X).
(17)
3.4 Emotion Correlation Loss
Finally, avoid excessive or erroneous introduc- tion of emotional information, we construct an emo- tion correlation loss for regular constraints:
exploiting multi-resolution emotions. 4) KEMP (Li et al., 2022b): model introducing external knowledge. 5) CEM (Sabour al., 2022): model leveraging commonsense draw more informa- tion. 6) SEEK (Li al., 2022a): model exploit- ing serial encoding and emotion-knowledee inter- action. For a fair and clear comparison, without otherwise stated, all models and model variants of our E- E-CORE and SOTAs are trained from scratch based on dialogue-level emotion annotations.
Leco = En.yee jij R R By]
(18)
where V is the learned co-occurrence emo- tions, taking top-3 emotions for soft strategy and vrelevont for hard strategy. Obviously, minimizing Leco loss prevent to introduce multi-emotion with low correlation weights, as low weights indicate the emotions are unlikely to occur in the same context. Considering above all components, joint loss function is adopted as the overall optimization ob- jective to achieve end-to-end paradigm learning:
Our model is explored using soft and hard strate- gies respectively, as introduced in Sec.3.3, denoted as Ours(Soft) and Ours(Hard). The model based on the transformer (Vaswani et al., 2017) frame- work with 4 blocks and heads, with the emotion graph of layer L = 2 and resolution level K 3, corresponding to the threshold [0, 0.07., 0.15]. The parameters for loss function are "/1 +/2 1. More implementation details are covered in ap- pendix D.
L = Leen +
Lemo P2Cco
(19)
4 Experiment Settings
4.1 Datasets
We evaluated our E-CORE on the EMPATHETIC-
4.3 Evaluation Metrics
DIALOGUES (Rashkin et al., 2019) dataset, which is collected from Amazon Mechanical Turk and contains about 25k open-domain dyadic conversa- tions. Each conversation comes from speaker and listener, in which the speaker is asked talk about personal feelings, and the listener responds empathetically. We split the train/val/test set into 19, 533/2, 770/2, 547 conversations.
Following previous works, for response generation, adopt perplex- ity (PPL) (Serban et al., 2015) and distinct-n (Dist- n) (Li al., 2015) as the main automatic metries which measures the quality and diversity of gen- erated responses, respectively. For emotion per- ception, we employ the emotion accuracy (Acc) to measure the consistency between predicted main- emotion and ground-truth emotion.
Automatic Evaluation.
In addition, to further validate the fidelity of the E-CORE in emotion correlation modeling, we also construct sub-dataset with multi-emotion annota- tion. This sub-dataset is obtained by: 1) emotional annotating with large-scale language models Chat- GPT(OpenAA, 2022) and ChatLLaMa(NebulyAAA 2023) on the above test set; 2) screening the sam- ples that have identified the ground-truth emotion and contained multi-emotion labels; 3). filtering the mistaken annotation with manual inspection. This sub-dataset composes of 739 samples, with average of 2.93 emotion labels per sample.
Human Evaluation. To test the model's ability on generating human-like responses, we conduct human ratings to evaluate the generated responses from three aspects: Fluency (fluency of responses), Relevance (relevant to dialogue context) and Em- pathy (empathetic expression of responses). We randomly sclect 100 dialogues, paired with the dia- logue context and responses from the baselines and our E-CORE, Three human annotators are asked score the selected instances on three metrics in the range of [1, 5], with the higher the better. The av- erage scores of all annotators are the human rating
dSub-dataset detailed in appendix H.

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Automatic Evaluation
Human Evaluation
Models
PPL
Dist-1
Dist-2
Acc
Fluency
Relevance
Empathy
Transformer (Vaswani et al.. 2017)
37.73
0.47
2.04
3,76
3.32
3.14
MIME (Majumder et al., 2020)
37.09
0.47
1.91
34.24
3.82
3.64
3.35
EmpDG (Li al., 2020a)
37.29
0.46
2.02
34.31
3.79
3.67
3.46
SEEK (Li et al., 2022a)
37.37
0.70
3.13
38.90
3.86
3.78
3.54
KEMP (Li et al., 2022b)
36.89
0.55
2.29
39.31
3.88
3.86
3.62
CEM (Sabour et al., 2022)
36.11
0.66
2.99
39.11
3.92
3.77
3.60
Ours(Soft)
33.04
0.68
3.38
42.57
3.94
3.96
4.02
Ours(Hard)
33.03
0.72
3.49
42.59
3.92
4.00
4.08
Table Comparisons with SOTAs.
suggests that the performance is better with a lower score.
Models
Win
Loss %
Models
PPL-
Dist-1
Dist-2
Acc
R
RØ3
R
Ours(Soft) vs KEMP
38.6
19.1
42.3
MIME
0.79
Ours(Soft) vs CEM
37.9
19.4
42.7
EmpDG
35.27
1.23
3.20
33.17
0.52
0.84
1.07
SEEK
35.14
1.78
6.54
34.22
0.51
0.88
1.12
Ours(Hard) vs KEMP
42.6
19.5
37.9
KEMP
34.56
1.39
4.27
36.37
0.58
1.02
1.32
Ours(Hard) vs CEM
42.9
19.7
37.4
CEM
34.52
1.64
5.37
36.26
0.57
0.98
Ours(Soft)
Ours(Soft) vs Ous(Haadd)
26.7
32.1
41.2
Ours(Hard)
31.18
2.11
8.62
40.46
1.47
Table Comparisons with SOTAs on human A/B test.
Table Comparisons with SOTAs on the sub-dataset.
results. In addition, for more direct model compari- son, also conduct the human A/B test with the best-performing SOTAs. Three annotators re- quired carry out pairwise response comparisons, selecting better response for each instance. Tie allowed both are good or bad. More details for human evaluations are covered in appendix I.
E-CORE generates more relevant comments rich in diversity, as more sufficient emotional informa- tion is provided. The great promotion of emotion accuracy (8.34% in relative, 3.28 in absolute for Acc) proves that, adopting correlation-aware aggre- gation rather than simply separating emotions, is beneficial for main-emotion perception.
5 Results and Analysis
Human Evaluation. The right part of Tab. presents the human rating results. E-CORE achieves better performance all aspects, espe- cially relevance and empathy. The great im- provement in relevance and empathy indicates that our model with emotion correlation learning helps provide more relevant emotions guidance, yielding more humanized and empathetic responses rich in relevant emotions and semantics. In addition, the pairwise comparison results in Tab.2 also confirm that the responses generated by E-CORE are pre- ferred by human judges, either using soft strategy or hard.
We conduct experiments on the benchmark dataset to verify the promise of emotion correlation learn- ing in both emotion perception and empathetic generation. Then investigate the ability of co-occurrence emotions recognition on the multi- emotion annotated subset, to further validate the essence emotion correlation learning in our method E-CORE.
5.1 Comparison with State-of-the-Art
Automatic Evaluation. SOTAs are mainly trained from scratch, we report the results trained from scratch for comparison fairness in Tab. 1.3 Our proposed E-CORE exhibits better performances than SOTAs on all automatic metrics, verifying the effectiveness of emotion correlation modeling in empathetic understanding. The significant im- provements response quality (8.53% in relative, 3.08 in absolute for PPL) and diversity (11.5% relative, 0.36 absolute for Dist-2) show that
Results on Sub-dataset. The current evalua- tion confirms that, even for EmpDG under single- label guidance, our model effectively achieves co- occurrence emotions learning with correlation mod- cling. To further validate the ability of our multi- emotion learning,
conduct extensive experi- ments on the multi-emotion annotated sub-dataset. using the metric Recall @k for quantitative evalu- ation, which indicates the number of ground- -trtth emotions covered by the top-k predicted emotions
"Results on pre-trained model are supplied in appendix E.

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Figure 4: Visualizations of emotion correlation for dataset and E-CORE. Displayed edges are between emotions with correlation weights greater than 0.3 after maximum-value normalization. Same emotions in (a) (b) are highlighted in the same color, which is marked based on the emotion distribution in the dataset.
Models
PPL-
Dist-1
Dist-2
Acc
Emotion
Sentimental
Context
Speaker,:
through of my old stuff yesterday, and found my security blan-
Ours(Soft)
33.04
0.68
3.38
42.57
w/o graph
36.42
0.51
2.30
40.36
ket that used when was a kid!
w/o co-p 35.92
0.53
2.38
39.26
MIME
1 am sure you will do great.
EmpDG
That is so sweet.
w/o co-loss
35.36
0.56
2.71
38.15
SEEK
That is so goeat.
Ours(Hard)
33.03
0.72
3.49
42.59
KEMP
glad you are able get fixed.
w/o graph 36.44
0.52
2.98
39.83
CEM
sure you will get good time.
w/o co-p 35.71
0.51
2.55
39.92
Ours(soft)
That is so nice of you to go hack memorics.
Ours(hard)
I allso love those moments.
w/o co-loss
35.50
0.50
2.36
38. 38.19
(Relevant Emotion: Sentimental, Nostalgic)
w/o co-g
35.45
0.54
2.70
42.00
Gold
Awww bet that brought back memories.
Table 4: Results ablation studies.
Table Case study of the generated responses our E-CORE and the baselines.
(or the predicted relevant emotions for hard strat-
egy). The great promotion shown in Tab.3 (44.1% in R@3, 31.3% @)5) reflects the significant su- periority of E-CORE on multi-emotion learning. In addition, a greater improvement in original metrics (11.6% in Acc), further proves that our E-CORE has stronger learning ability for complex samples with multiple emotions over SOTAs.
following variants: 1) w/o graph: the model without multi-resolution emotion graph, which di- rectly implements other modules with the vanilla transformer framework. 2) w/o co-p: the model without correlation-awal aggregation in emotion perceptron. 3) w/o co-g: the model without correlated co-occurrence emotions guidance generator, which not uses soft/hard strategy and generates responses with main-emotion. 4) w/o co- loss: the model without emotion correlation loss. As reported in Tab.4, 4, modules make reason- able contributions to E-CORE. For learning. re- placing the emotion graph with transformer causes significant performance degradation, verifying the effectiveness of multi-resolution emotion graph for emotion correlation learning. For utilizing, mod- els without correlation utilizing on perceptron generator respectively perform weakly in emotion accuracy and response quality, indicating that our designed aggregation and soft/hard strategies ef- fectively incorporate the correlated co-occurrence
Further, we visualize the emotion correlation of the dataset and E-CORE for a more intuitive com- parison. Among these, the correlation weight for the dataset calculated based on the co-occurrence counts of emotion pairs, and for E-CORE is directly using the learned weight R after model training. As shown in Fig.4, our model shows very close emo- tion correlation real distribution, proving the accuracy of emotion correlation modeling.
5.2 Ablation Study
To fully examine the contribution of each design in Our E-CORE for addressing corresponding chal- lenges, we conduct ablation studies through the

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emotions to enhance empathetic perception and ex- pression. Finally, the result without correlation loss proves its importance for global supervision. More ablation studies and analyses in appendix J.
formation provide more effective supervision for multi-emotion learning still needs be considered. This is also a common problem faced by many emotion-relatee generation tasks. The ablation study on the model without response reconstruction loss supervision shown in appendix J indicates that the supervision for multi-emotion partly sources from the empathetic response, which may serve an improved inspiration. 2) Secondly, all existing methods are evaluated on the unique benchmark dataset EMPATHETICIDALLOOUUU (Rashkin et al., 2019). , As empathetic dialogue generation an emerging task, only one relevant English dataset has been proposed, lacking of datasets in more lan- guages and categories for reference. 3) Finally, we observed in the experiment that the existing mod- els tend to generate generic responses, especially for complex hard samples, which are difficult capture the key points. Therefore, the learning of hard samples is also a developing direction of the empathetic dialogue generation task.
5.3 Case Study
As the case in Fig. has shown the ability of E- CORE jointly guide generation with captured very different co-oceurrence emotions (afraid and grateful), Tab.5 exhibits case with similar co- occurrence emotions for comprehensive qualitative analysis. As the speaker expresses sentimental for "old stuff", relying on the significant correlation between sentimenial and nostalgic, our E-CORE successfully identifies the auxiliary emotion nos- talgic, generating more relevant phrases "go back memories" and "those moments", while the base- line models only produce universal responses. In general, whether similar or distant co-occurrence emotions are significant for EmpDG, which all help for global and detailed empathetic expression. Our E-CORE with emotion correlation learning helps provide sufficient emotion guidance, yielding more humanized responses rich in empathy.
Ethics Considerations
The empathetic dialogue dataset (Rashkin et al., 2019) used in our paper publicly-available and annotated through Amazon Mechanical Turk, which means it totally protects the privacy of real users. Besides, we make sure the anonymization in the human evaluation process. We believe our research work meets the ethics of EMNLP.
6 Conclusion
In this paper, we propose exploit the intrinsic emotion correlation in dialogues to enhance empa- thetic dialogue generation. distinctive framework with three effective modules respectively address- ing the emotion correlation learning, utilizing, and supervising, is designed. Extensive experiments on the benchmark dataset prove the significant advan- tages of our framework in improving emotion per- ception and empathetic generation. Specific analy- sis further demonstrates the accuracy of our emo- tion correlation learning. In the future, our work can inspire other approaches to explore emotion- related tasks with multi-emotion correlation learn- ing. without being limited by single-emotion label.
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A Statistics for Dialogue Emotions
C Modified Transformer Decoder
To verify the importance of the multi-emotion cor- relation for the empathetic dialogue generation task, we make statistics on the quantity of emotion- related words of other emotions contained in the dialogue samples of the benchmark dataset EM- PATHETICDIALOGUES (Rashkin et al., 2019), to preliminary observe the emotions co-occurrence situation in the dataset. Specifically, our annotators provide the annotation of high-frequency emotion- related words for all 32 emotions in the dataset. By counting the number and frequency of emotion- related words of different emotions in the dialogue, we can roughly inform the co-occurrence situation of various emotions in the dataset. The annotated emotion-related words of 32 emotions are shown in Tab.
In this section, we provide detailed introduction for the modified transformer decoder used the soft/hard gated generator:
8: Dec (Ya hx hnode).
(22)
Taking masked response Y<t [30. Vt- 1], con- textual representation hx. improved graph node feature hnode as the input, the detailed implementa- tion is:
h'x MH-ATT(ya hx),
(23)
Inode > (haode));
(24)
8, = LayerNorm(yat + Wa(h | [bmode)):
(25)
8t LayerNorm($ + FFN((;)),
(26)
B Details of Hard Strategy
where MH-ATT and FFN denote multi-heaa atten- tion layer and feed-forward network respectively. Through a simple concatenation operation, the modified transformer decoder effectively introduce the graph node feature which is rich in emotion- interacted semantic information into the decoding process.
In this section, we elaborate on the hard strategy, which adopts the OTSU algorithm. i.e., maximizing the variance between the two categories to divide emotions into irrelevant and relevant categories:
Vrelenant virrelenant = OTSU Ve). (20)
D Implementation
Taking final emotion attention hemo the emotion attention value for emotion nodes Ve, specifically, the above equation can be written in detail:
The model is implemented in PyTorch (Paszke et al., 2017) with a single NVIDIA GeForce RTX 3090 GPU, and trained for about 10 epochs with batch size 16 and dropout rate 0.2. The train- ing time of E-CORE is about hours for around 26000 iterations. The vocabulary size 23, 714, and use pre-trained Glove vectors (Pennington et al., 2014) for word embedding initialization. Our model is optimized by Adam optimizer (Kingma and Ba, 2014) and the learning rate is changed dur- ing training according to Vaswani et al. (2017) with the final learning rate is 3.5c-4. We also introduce the commonsense knowledge and the label smooth- ing strategy used in the SOTA model (Li al., 2022b) as a trick to improve performance, without losing comparative fairness.
prelevent arg
|| 11
(21)
11). V = Cv. V.
+
Where 1 . is the mean attention values of emotion nodes in the corresponding set Ve, V, V. In the formula, there is no obvious distinction be- tween V and V. and set the part with larger attention values corresponding to V.
Obviously, for N emotions, there are at most segmentation thresholds, as the emotions with a higher attention feature than the threshold will be regarded as the relevant category. Based on the statistical results shown in Fig.2, there almost no five or more emotions in the dialogues in the dataset. To facilitate the operation, only the first five segmentation thresholds are considered, com- paring their cater-cat variance to obtain the optimal division of emotion.
E Experiments based on Pre-trained Model
As SOTAs are all trained from scratch, for com- parison fairness, we mainly report the results model trained from scratch in the main body. this section, we further explore our E-CORE and

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Models
PPI.
Dist-1
Dist-2
Acc
Firstly, based on the large-scale pre-trained lan- guage models ChatGPT (OpenAI, 2022) and
DFMP-Dia
15.21
2.79
4.24
46.43
CEM-DialoGPT
15.06
2.88
4.52
46.20
ChatLLaMa (Nebuly-Al, 2023), we conduct emo- tion annotation for all 2, 547 conversations the 50.12
Ours(Soft) DialoGPT
13.02
4.91
50.04
Ous(Haadj-DialoGPP
12.94
3.07
4.96
test set of EMPATHETICDDAL.OGN LOGUES, obtaining an intermediate dataset of 1, 536 samples labeled with multiple emotions. Secondly, we screen to- tal of 1, 097 samples that successfully identify the ground-truth emotion, which proven to have higher annotation quality. Finally, a manual ex- amination is conducted to filter out mistaken an- notations. The final dataset is composed 739 samples, with an average of 2.93 emotion labels per sample. Tab. 11 shows some dialogue samples of this auxiliary dataset.
Table 6: Comparisons results based on pre-trained lan- guage model.
Models
PPL
Dist-1
Ours(Soft)
33.0410022
0.68::001
3.38:0.02 0,72++002
42.57:10.13 42.59410.09
Ours(Hard)
33.03440.24
3.49-0004
Table 7: Results of variance on all evaluation metrics.
SOTAs using& a pre-trained language model Di- aloGPT (Zhang et al., 2020b). As shown in Tab.6, all models have significant improvement while us- ing the pre-trained language model, indicating that the huge amount of pre-trained dialogue datasets is beneficial for the empathetic dialogue genera- tion task. Besides, our E-CORE consistently shows superior performance over SOTAs, which demon- strates the advantages of our model over SOTAs whether uses pre-trained model or not.
Details of Human Evaluation
To evaluate the model's ability on generating human-like responses, we conduct experiments on human evaluation from three aspects: Fluency, Relevance and Empathy. Three human annota- tors are asked to score the instances on these there aspects in the range of [1.5]. We use Spearman's 's Rank correlation coefficients to evaluate the agree- ment among the annotators. The coefficients be- tween any two annotators are all near 0.6 and at an average of 0.64, which shows the consistency and reliability of human evaluation scores.
F Experiments on Stability Testing
To verify the stability of the model, we evaluate the variance and statistical significance for E-CORE. Specifically, we adopt 5 different random seeds to conduct experiments on our model, based on soft and hard strategies respectively. Tab.7 reports the variance of all evaluation metrics, verifying the performance stability of the model.
In the following, we further provide the guide- lines regarding how to judge the quality of the model's result these three aspects in terms of different features.
G Statistical Significance
I.1 Fluency
This metric measures the fluency of the model's result. The definitions of different scores are:
For statistical significance, we conduct one-side Student's t-test, proving our model (soft) signifi- cantly outperforms the best-performing baseline CEM with 5.68e-5 (p < 0.05 indicates that the hypothesis that A (E-CORE) outperforms B (CEM) is significantly valid). These conclusions hold for hard strategy.
[5]: The generated responses are human-like, grammatically correct, fluent, and very easy understand.
[4]: Choose this score when you are hesitant between the score and score 5.
H Sub-dataset with Multi-emotion Annotation
[3]: The generated responses have few gram- mar errors, but not hinder understanding.
We construct sub-dataset with multi-emotion an- notations an auxiliary test of the benchmark dataset IMMATHETICDIALOGUUS accuracy of emotion correlation modeling our E-CORE. In this section, we will provide a detailed explanation for this sub-dataset.
[2]: Choose this score when you are hesitant between the score and score 3.
verify the
[A]: The generated responses have numerous grammar errors and difficult to understand.

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Models
PPL
Dist-1
Dist-2
Ace
Models
PPL
Dist-1
Dist-2
Ace
(urf(Soft)
33.04
0.68
3.38
42.57
Our(SSfi)-SEEP
33.04
0.68
3.38
42.57
w/o gate-g
35.11
0.58
3.01
42.02
VAD
33.39
0.66
3.36
42.28
VADER
33.35
0.65
3.32
42.17
w/o gen-loss
34.40
0.61
2.73
39.37
SentiWordNet
33.46
0.63
3.34
42.05
(Hrr()
33.03
0.72
3.49
42.59
w/o
34.97
0.54
40.06
w/o gate-g
35.32
0.56
3.07
42.13
Ours(Hard)-SKE
33.03
0.72
3.49
42.59
w/o gen-loss
34.74
0.57
2.50
39.83
VAD
33.35
0.68
3.44
42.35
VADER
33.32
0.66
3.42
42.07
Table 8: More results on ablation study.
SentiWordNet
33.42
0.65
3.38
42,02
w/o
34.91
0.57
3.06
40.12
1.2 Relevance
Table More ablation studies on the performance of emotion intensity, where SKEP is used for original method.
This metric measures the informativeness and rel- evance of the model's result. The definitions of different scores are:
More Ablation Study
[5]: The generated responses are perfectly related to the dialogue context.
To further examine our E-CORE, more ablation studies are conducted through following variants: 1) w/o gate-g: for testing the sub-module soft/hard gated generator, the model without gated attention, directly using the main emotion feature for guidance. 2) w/o gen-loss: for testing the sources of emotional supervision, the model with- out response generation loss Egen.
[4]: Choose this score when you are hesitant between the score 3 and score 5.
[3]: The generated responses to some ex- tent related to the dialogue context.
[2]: Choose this score when you are hesitant between the score and score
As we can see Tab.8, all sub-modules con- tribute a lot to the whole model. The gated atten- tion mechanism has a great impact on generation, suggesting that gated attention is helpful for mean- ingful emotions extracting, which further provide more sufficient emotional guidance enhance pression. is worth noting that models without response generation loss (w/o gen-loss) not only show a decline the generation, but also perform poorly in the emotion prediction (42.57 to 39.37 in Acc), indicating that emotion supervision not only comes from the dingle-emotinn label, but also from the empathetic responses, further proving the reli- ability of our emotion correlation learning, which modeled by emotion graph with context-based in- teraction capturing and multi-sample joint transfer learning.
[1]: The generated responses are completely unrelated to the dialogue context.
1.3 Empathy
This metric measures the empathy of the model's result. The definitions diffcrent scores are:
[5]: The generated responses are rich in emo- tional expression, and the expressed emotions perfectly correspond to the dialogue context.
[4]: Choose this score when you are hesitant between the score 3 and score
[3] The generated responses to some extent contain the emotional expression, and the ex- pressed emotions to some extent correspond to the dialogue context.
J.1 More Ablation Study Emotion Intensity
[2]: Choose this score when you are hesitant between the score I and score 3.
We also conduct additional ablation studies to eval- uate the performance of emotion intensity, by com- paring the variants without emotion intensity with different emotion intensity labeling on both soft and hard strategies. The different emotion intensity values are obtained by four different emo- tion analysis models, specifically: SentiWordNet
[1]: The generated responses do contain the emotional expression, or the expressed emotions do not correspond to the dialogue context.

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Emotion
Proud
Context
Sr: Lately have felt proud of my success newly single mother. gets lonely sometimes, but can honestly say have been doing everything and more.
Gold
Keep up the good work! You will do good am happy for you.
MIMI
SEEK
That is grea for you.
EmpDG
That is great! hope you get it!
KEMP
That is great to fiear! I hope you can find new job.
CEM
That is good to hear.
Ours(soft)
am you
great parent!
Ours(hard)
am you will proud of yourself.
(Relevant Emottin: Proud)
Emotion
Faithful
Context
Sr: My hushand is the faithful man.
That great hear! faithful spouse is blessing.
Sz:
have so many health problems and he is always there for many no what being loving and caring.
Gold
am sorry to hear about that! hope everything gets befter for you!
MIME
Oh no! That is terrified for you.
EmpDG
That is great for you!
SEEK
Thöpe you can get better.
KEMP
agree with you. am sure he will have great relationship.
CEM
That is good. I hope he gets better.
Suff(Soft)
That is great! Hoope you can get through that!
Ours(Hard)
That great! hope you have great life!
(Relevant Emotion:
Faithful, Hopeful)
Table 10: More studies of the generated responses by E-CORE and the baselines. Key words in context and responses of different emotions are highlighted in different colors. S and L respectively correspond to the i-th sentence from the speaker listener.
(Sebastiani and Esuli, 2006), VADER (Hutto and Gilbert, 2014), VAD (Zhong et al., 2019), SKEP (Tian et al., 2020).
through" or "great life" for hopeful, while other baselines either take a wrong understanding or only notice one emotion. In general, the emotion corre- lationn learning enhances the emotion-interactte semantic understanding. resulting in more human- ized responses rich in information and empathy, whether in simple or complex dialogue contexts.
As we can see from Tab.9, the model without emotion intensity, which degenerates into single- resolution emotion graph, performs weakly, indi- cating the great impact of multi-resolution graph modeling for correlation learning. Furthermore, the model exhibits strong robustness to different emotion intensity labeling, also indicating that the emotion correlation learning more relies graph training, instead of the performance of the original emotion intensity.
Visualization Analysis
To further explore the working mechanism of our emotion graph, we visualize the edge features from dialogue word nodes to 32 emotion nodes for the case of 1ig.1 As shown in Fig.5, our Ours(Soft) puts the highest attention on the words contain- ing informative meaning, among which the words "terrified", "hit" and "drunk driver". contribute emotion terrified and afraid, as the words "glad" and "alive" pay more attention to grateful. We can conclude that our mestitresolltion emotion graph effectively learns diverse emotional information.
More Case Studies
Typically, two more generation cases of single- emotion guidance and double-emotion guidance are shown 10..10. In the first case, E-CORE extracts the key information "proud" and "single mother" from the context, and generates more detailed and accurate phrases "great parent" and "proud", while the baseline models only predict generic phrase "great". In the second case, the speaker expresses her husband's "faithful" and her "health problems". Our E-CORE successfully de- tects two emotions in the dialogue context, gener- ating the praise "great" for faithful and wishes "get
In addition, we also explored the effect of correlation aggregation for emotion percep- tion, and visualize the working mechanism in Equa- tion based on the same case above. As can see from Fig.6, for the initial graph word-emotion edge fusion features, similar emotion terrified was mistakenly selected while the ground-trui -ruth emotion

---

is afraid. Although afraid is already very close, for the existing methods, it will be discarded while it recognized as a secondary emotion. How- ever, in our E-CORE, after carrying out correlation- aware aggregation, the greater weight of grate- ful for afraid assists in identifying the afraid as the main-emotion, achieving full utilization of all co-occurrence emotions. This further confirms the effectiveness of correlation-based emotion occurrence learning in enhancing emotional per- ception, especially for hard samples with similar emotions.

---

Emotion
Jealous, Grateful, Hopeful
Context
Sr:
My sister recently paid off her house. felt envious of her have been trying to
pay mine down.
1,: That will be such wonderful feeling when you do. know it is hard when you see someone reach the finish line first. Are you able to turn that envy into inspiration?
Sy: I really am trying hard to. That such a great suggestion. know am making good progress to my goal, and genuinely am pleased she did it!
L2: See? That is terrific! And she 's got to love that you can celebrate her victories too inspiration support yours!
Emotion Context
Disappointed, Content, Joyful
S1: We went
vacation about week ago to the beach and it rained several days while we were there. We wanted to go to the beach every day!
Lt: hate when that happens! Especially when you have been waiting for the beach!
Sz: Yes, and when you pay a fortune to get beachfront! But, we still had good time, just wished we could have had less rain!
L:: That what matters most, that you had good time and made memories!
Emotion Context
Sentimental, Nostalgic
Sy: I was going through some boxes the other day. found some old pictures of my kids I thought were gone.
L That is exciting! love having pictures to look back on.
S2: Yes, definitely! It really made us all start talking about those and other memories, good times.
L2: love being able to reminisce on the past. Time goes by so fast.
Emotion Context
Hopeful, Anticipating, Proud
Sr: I have been extremely diligent in my studies so far, and am participating in two clubs this upcoming year. The future is looking bright!
L₁: That is awesome. What are you hoping to do?
Sy: I am electrical engineering student, and i am interested in quite few things at this point. think after this year, 1 will be able to get paid internship next summer!
12: That the dream, to get paid in college. wish could have done that. Congratulations!
Emotion Context
Confident, Proud Impressed
Sr: My daughter is such talented and creative young artist. When she was nominated to be 'most artistic" at school, I felt very secure she would win, because she really is amazing!
L1: Thats awesome. Did she?
Sy: She did! I know am the parent so can be biased, but it is really so impressive how well she draws, paints, shades
L2: Bet you are so proud. I would definitely be.
Table Sub-dataset. Example dialogues in our construced
annotated subset. The ground-trute emotion is highlighted in red. S, and L, respectively correspond to the i-th sentence from the speaker listener.

---

surprised
surprised, astonish, astound, amaze, startle
excited
excited, aroused, ecstatic, elated, rapturous, cuphoric, exhilarated
annoyed
annoyed, irritated, miffed, nettled, peeved, pissed, riled, roiled, stung proud, gallant, lofty, majestic, arrogant, haughty angry, mad, indignant, cross, irate
proud
angry
sad
sad, deplorable, distressing, lamentable, pitiful, sorry
grateful
grateful, gratitude, grate, thankful, appreciative
lonely
lonely, alone, lone, solitary, unfrequented, lonesome, desolate
impressed
impressed, awed, overcome, overwhelmed, dazzled, stunned
afraid
afraid, scared, alarmed, petrified, fearful
disgusted
disgusted, sick of, tired of, outraged, appalled, offended, sickened, scandalized
confident
confident, sure, convinced, certain, positive terrified, panicky, stricken, fright, frighted hopeful, optimistic. promising, hope, aspirant, bright
terrified
hopeful
anxious
anxious, concerned, nervous, uneasy, worried
disappointed
disappointed, defeated, discomfited, foiled, frustrated, thwarted
joyful
pleasant, agreeable, cheerful, joyful, elated, gleeful, jubilant
prepared
prepared, ready, disposed, inclined
guilty
guilty, criminal, blameworthy, fault, culpable, hangdog, shamefaced, shamed
furious
furious, enraged, ferocious, fierce, savage, infuriated, maddened, raging, tempestuous, wild
nostalgic
nostalgic, homesick, wistful, maudlin, regretful
jealous
jealous, envious, covetous, envy
anticipating
anticipating, prediction, prevision, expect, prescience, expectation
embarrassed
embarrassed, awkward, cringe, abash, blush, discomfiture, chagrined
content
content, satisfy, gratify, meet, satisfied
devastated
devastated, demolish, destroy, ravage, raze, ruin, wreck, desecrate, desolate, despoil sentimental, slushy, maudlin, bathetic, mushy, schmaltzy, soppy caring, lovingness, loving, care, affectionate, sympathetic trusting, trust, believable, believe, trusty
sentimental
caring
trusting
ashamed
ashamed, mortified, humiliated, abashed apprehensive, perturbed, troubled, agitated, uneasy, perturbed faithful, loyal, reliable, staunch, fidelity, honest, allegiance, faith
apprehensive
faithful
Table 12: The emotion-related words of all 32 emotions for dataset EMPACHETTCDIALOOGUSS

---

felt
bit
terrified
when
was
by
drunk
driver
last
year
am
very
glad
to
be
alive
to
today
Ali
dccttee
pnooyea
pnoud
anrry
pes
grateful
loneyy
impresse
afraid
confidant
férrified
hopeful
anxious
disappo
prepate
Quilyy
furious
nostalgic
lealous
anticipating
content
devastater
Cantimern
Cussiig
bus ashal spprehentti
Rehamme
Figure The visualization the fusion of word-to-emotion edge features for E-CORE.
Smaple-specific Emotion Correlation Weights
aecbed
s1noyed
surprised
annoydd
proud
excited
proud
proud
galieul
angry
5g0
grateul
Itrely
grateful
lonely
bupressed
afraid
atradd
daquste
ponfident
contident
terrifee
"MA-""
hopetu
hopehl
araious
disappoineed
ansious
X
joyful
douppo
sonut
prepared
guity
Pyful
imppree
nostalgic
jealous
anticipating
nosta gi
embarrassed
zalus
content
jalcus
devastated
sentimental
caring
conett
contert
deepemeed
ashamed
derestated
apprehensive
faitiful
arng
buiing
Regudum
Polouus
moat
Audue
fraug,
Fasson
smozue
18666
Anno
smoury
buged aovedatooee
passeunquu
Dossmu
busing
sppnetrnouu
spreinniiee
NCMU
Figure 6: The visualization of the correlation-awee aware aggregation for emotion features in E-CORE.


Reviews:
[{"review_id": "uNzRntwAiD", "reviewer": "Reviewer_9bdy", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer 9bdy,\n\nThank you for your valuable review. We are encouraged that you found our approach novel and technically sound. We provide detailed answers to your comments below. Hope to address your concerns.\n\n----\n> **R1:** In the introduction and the experimental analysis, the authors marked the magnitude of the metrics enhancement（8.53% in PPL，16.7% in Dist-2，8.34% in Acc），It is suggested that the change be made to the absolute value of the enhancement rather than the percentage to reduce ambiguity.\n\nFollowing your suggestion, we will provide both the absolute values and percentages of the enhancement, where absolute values more directly reflect the model's breakthroughs in various metrics on response quality and emotion accuracy (3.08 in PPL, 0.5 in Dist-2, 3.28 in Acc), and percentages more intuitively reflect the improvement of our model compared with SOTAs (8.53% in PPL, 16.7% in Dist-2, 8.34% in Acc). \nThanks for your valuable suggestion. We will provide absolute values as the main explanation for performance improvement, with percentages as additional, and provide clear explanations for both to avoid misunderstandings.\n\n\n----\n> **R2:** The performance of emotion intensity labeling has an impact on the subsequent calculation of sentiment correlations was not clarified in the paper (e.g., the relationship between the loss generated when the model is labeled with emotion intensity and the overall loss of the model proposed by the authors).\n\nThe emotion intensity **indirectly impacts** the calculation of emotion correlation **by influencing the graph construction**. Specifically, emotion intensity indicates the emotional importance of dialogue words, guiding the word connections at different resolutions and serving as initial edge weights, enabling the emotion graph to capture the emotion interactions from multi-resolution, further encoding the emotion correlation. Words with higher emotion intensities tend to have a greater effect on the emotion graph, thus playing a greater role in the subsequent calculation of emotion correlations.\n\nFor the case you provided, the loss generated when the model is labeled with emotion intensity and the overall loss of the model should be the same, as the model is already labeled with emotion intensity. To avoid not fully understanding your question, we **supplement all ablation experiments on the performance of emotional intensity**, by comparing the variants without emotion intensity or with different emotion intensity labeling on both soft and hard strategies. The results are shown in Table 1 below. \n\nTable 1: Ablation studies on the performance of emotion intensity based on both soft and hard strategies. [1,2,3] are other emotion lexicons used to provide different emotional intensity labeling, and [4] is used in the original paper.\n\n| Model            |   PPL         |  Acc  |      Dist-1      |      Dist-2      |  \n|------------------|:-----------:|:-----------:|:-----------:|:-----------:|\n| Soft-w/o       |  34.97   |  40.06   | 0.54  |   3.02   |  \n| Soft-SentiWordNet [1]         |   33.46   |42.05 |   0.63 |     3.34  |   \n| Soft-VADER  [2]      |    33.35   | 42.17  |     0.65  |   3.32   |  \n| Soft-VAD  [3]        |   33.39  |  42.28   |     0.66  |    3.36  |    \n| **Soft-SKEP [4] (Ours)** | **33.04** |**42.57**|  **0.68** | **3.38** | \n| Hard-w/o       |  34.91   | 40.12   |  0.57  |   3.06   |  \n| Hard-SentiWordNet [1]         |   33.42   |42.02 |   0.65 |     3.38  |   \n| Hard-VADER  [2]      |    33.32   |42.07  |      0.66  |   3.42   |  \n| Hard-VAD  [3]        |   33.35  |  42.35   |   0.68  |    3.44  |      \n| **Hard-SKEP [4] (Ours)** | **33.03** |  **42.59**| **0.72** | **3.49** |\n\nThe results show that: 1) model without emotion intensity, which degenerates into a single-resolution emotion graph, performs weakly, indicating the great impact of multi-resolution graph modeling for correlation learning. 2) **model exhibits strong robustness to different emotion intensity labeling**, also indicating that the emotion correlation learning more relies on graph training, instead of the performance of the original emotion intensity.\nIf there are any questions for you, we welcome more specific explanations in the subsequent discussion stage and will actively respond. \n\n\n\n----\n> **R3:** The paper mentions that emotion graphs require modeling the correlation of intrinsic emotions and interconnecting all emotion nodes, and that the details of the main emotion and secondary emotions conversions should be detailed when calculating the emotion correlations and subsequent generation.\n\nWe would clarify that the **main emotion and secondary emotions conversions do not explicitly occur in the calculating of emotion correlations and subsequent generation, but occur in emotion signal perception**, as stated in Section 3.3. Specifically, for correlations calculating, all emotions are uniformly treated; and for subsequent generation, main emotion and secondary emotions are all considered as co-occurrence emotions to guide generation. So in these phases, emotions conversions do not occur. \n\nThanks for your suggestion. We will **provide more detailed explanations for the emotions conversions** in the emotion signal perception. Specifically, the model first obtains the original dialogue emotion scores by fusing the attention features of dialogue words to each emotion, then conducts the correlation-aware aggregation to obtain the final emotion scores. We provide a more intuitive explanation based on the case in Figure 1. Based on the original emotion scores, “terrified” is the main emotion and “afraid” “grateful” are the secondary emotions. After correlation-based reweighting and aggregation, due to the greater correlation weight from “grateful” to “afraid” than to “terrified”, \"afraid\" surpasses \"terrified\" to become the main emotion (Figure 6).\n\nIn addition, we also conducted experiments to calculate the proportion of the conversions between main emotion and secondary emotions (i.e., the proportion of main emotion changes before and after correlation-aware aggregation), as well as the absolute improvement it brings to emotion accuracy, as shown in Table 2.\n\n\nTable 2: The results of the proportion of emotions conversions and its absolute improvement in emotion accuracy.\n\n| Models | conversions proportion | Absolute Improvement in Acc |\n| -------- | :--------: | :--------: |\n| **Ours-Soft**    | 25.88%     | 3.31     |\n| **Ours-Hard**    | 15.09%     | 2.61     |\n\nThe results indicate that our correlation-aware aggregation strategy helps achieve a conversion from co-occurrence emotions (including main and secondary) to main emotion, which effectively improves the accuracy of main emotion prediction. \n\n\n----\n> **R4: The authors mention in the experimental section that their proposed model is compared to the Sotas model. As far as I know, the Baselines chosen for the paper is not the SOTA model. The authors can be confirmed by the following literature. Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection.**\n\nThanks for your suggestion. We have added a comparison for the work [5], as shown in Table 3 below. It is worth noting that [5] uses additional utterance-level emotion annotations for fine-grained emotion encoding, while our model only uses dialogue-level emotion annotations. Therefore, we would **explain from two settings: direct comparison with the SOTA [5], and the comparison with the SOTA [5] under same annotations, to clarify the advantages of our model compared to [5].**\n\n1). direct comparison with the SOTA [5]: **our model achieves the best performances in the most important metrics PPL and Acc, and near performances in Dist (-0.01 in Dist-1, +0.26 in Dict-2).** As PPL and Acc measure the consistency between generated responses/emotions and real responses/emotions, compared with Dist which measures the generation diversity, they can more directly and objectively reflect the model's ability to generate a relevant and empathetic dialogue.  **These results fully demonstrate that our model has achieved usually better response quality and empathetic perception for the Empathetic dialogue generation task**, even in a lack of information compared to [5], also achieves near performance in generation diversity. \n\n2). comparison with the SOTA [5] under same annotations: i.e., comparing with the SOTA(w/o Utter) model below, **our model outperforms the SOTA in all metrics, and exhibits a significant improvement in PPL(4.34 in absolute, 11.6% in relative) and Acc (3.69 in absolute, 9.5% in relative).** These results demonstrate the superiority of our model in empathic understanding and expression, when using the same input information.\n\n\nTable 3: Performance comparison with the SOTA model.\n| Model            |    PPL    | Acc      |      Dist-1      |      Dist-2      |          \n|------------------|:-----------:|:-----------:|:-----------:|:-----------:|\n| SOTA [5]    |37.03 |  41.85   | **0.73**  |   3.23   |  \n| SOTA (w/o Utter) [5]   |37.37 |  38.90   | 0.70  |   3.13   |  \n| **Ours-Soft**       |   33.04 |    42.57  |   0.68  |    3.38     |    \n| **Ours-Hard** | **33.03** | **42.59**|0.72 | **3.49** | \n\nFollowing your suggestion, we also added a detailed discussion on work [5]. This paper [5] proposed a serial encoding and emotion-knowledge interaction method for the task, effectively utilizing fine-grained emotion features and commonsense knowledge to enhance empathy response. As our method is distinct from [5], by modeling and utilizing the emotion correlation to achieve enhancement, we believe these two methods can be complementary to each other. \nWe will provide a detailed explanation and comparison of this work in the related work and experimental sections in the revision. Thank you for guiding us toward a more accurate discourse in our revised manuscript.\n\n\n\n\n\n\n\n\n**Reference:**\n\n[1] SENTIWORDNET: A publicly available lexical resource for opinion mining, ELRA, 2006.\n\n[2] Vader: A parsimonious rule-based model for sentiment analysis of social media text, AAAI, 2014.\n\n[3] Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations, EMNLP-IJCNLP, 2019.\n\n[4] Skep: Sentiment knowledge enhanced pre-training for sentiment analysis, ACL, 2020.\n\n[5] Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection, EMNLP, 2022.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The paper is well experimented and demonstrates meaningful enhancement of the experimental results through the use of advanced large-scale modeling and visualization and other work;\n2. The study is meaningful and innovative in improving the quality of dialog generation by introducing affective relevance;\n3. The paper is easy to understand and beautifully laid out.", "reasons_to_reject": "1.In the introduction and the experimental analysis, the authors marked the magnitude of the metrics enhancement（8.53% in PPL，16.7% in Dist-2，8.34% in Acc），It is suggested that the change be made to the absolute value of the enhancement rather than the percentage to reduce ambiguity;\n2.The performance of emotion intensity labeling has an impact on the subsequent calculation of sentiment correlations was not clarified in the paper (e.g., the relationship between the loss generated when the model is labeled with emotion intensity and the overall loss of the model proposed by the authors);\n3. The paper mentions that emotion graphs require modeling the correlation of intrinsic emotions and interconnecting all emotion nodes, and that the details of the main emotion and secondary emotions conversions should be detailed when calculating the emotion correlations and subsequent generation;\n4. The authors mention in the experimental section that their proposed model is compared to the Sotas model. As far as I know, the Baselines chosen for the paper is not the SOTA model. The authors can be confirmed by the following literature.\nEmpathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection", "questions_to_authors": "", "paper_topic_and_main_contributions": "Existing studies ignore the intrinsic emotion correlation in dialogues, resulting in inaccurate emotion perception and unsuitable response generation. Therefore, the paper propose a novel emotion correlation enhanced empathetic dialogue generation framework, which capture multi-resolution emotional interactions through dialogue history contexts and eventually complete empathic responses.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Missing_References": "Wang L, Li J, Lin Z, et al. Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection[C]//Findings of the Association for Computational Linguistics: EMNLP 2022. 2022: 4634-4645."}}, {"review_id": "hMCF2Zn4Rg", "reviewer": "Reviewer_9WcU", "summary": "", "questions": "1.\tAre all emotion nodes connected to each other for the graphs of all instances?\n2.\tI like the motivation of emotion correlation, but the implementation of the overall method is a little bit complicated. Do you compare your method with directly few-shot prompting LLMs? What are the advantages of your methods and what are the drawbacks of the current LLMs when performing empathetic response generation?\n", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer 9WcU,\n\nThanks for your insightful review. We are excited that you appreciated the contribution and the novelty of our work. We improve the expression as you suggested to improve clarity, and provide a detailed discussion on your questions. \n\n----\n> **R1:** It would be clearer to give a description of the overall process in the figure caption or the beginning of section 3 based on the contents of Figure 3.\n\nThanks for this constructive suggestion. We will supplement a description of the overall process in the figure caption of Figure 3. The details are as follows:\nThe overall process consists of 3 phases: 1)context encoding: to encode the dialogue context and all emotions into embedding features and contextual representation; 2)multi-resolution emotion graph network: to capture the context-based emotion interaction from different resolutions, further encoding the emotion correlation; 3)emotion correlation enhanced decoding: to incorporate the emotion correlation for enhancing emotion signal perception and response generation, respectively.\n\n----\n> **R2&Q1:** Are all emotion nodes connected to each other for the graphs of all instances?\n\nThank you for your concerns. The emotion nodes are **connected to each other in graph modeling, but may not be the case in decoding phase**. \nIn the multi-resolution emotion graph modeling phase, all emotion nodes **are connected to each other for the graphs of all instances**, to model the emotion correlation (Section 3.2).\nIn the emotion correlation enhanced decoding phase, when using the  hard strategy, the soft/hard gated generator improves the emotion graph by removing the context-irrelevant emotion nodes, so the emotion nodes **are not connected to each other** (Section 3.3 and Figure 3-b).\nWe will improve the expressions to provide clearer explanations.\n\n----\n> **R3&Q2:** I like the motivation of emotion correlation, but the implementation of the overall method is a little bit complicated. Do you compare your method with directly few-shot prompting LLMs? What are the advantages of your methods and what are the drawbacks of the current LLMs when performing empathetic response generation?\n\nThanks for your constructive suggestions. We **have compared our model combined with full pre-trained LLM (DialoGPT [1])** in Appendix E, but not compared it with directly few-shot prompting LLMs, as we think full pre-trained LLM exhibits better performance than the few-shot one, so the comparison is conducted with the better one.\nAccording to your suggestions, **we conducted a detailed comparison of our model and directly few-shot prompting LLMs (DialoGPT [1], ChatGPT [2]). As shown in Table 1, Our model outperforms DialoGPT in all performance, but still falls short of ChatGPT in response quality**. We will further **conduct a detailed analysis** of the comparison and **then summarize the advantages** of our model.\n\nTable 1: Performance comparison with few-shot prompting LLMs. Using metric BLEU instead of PPL to evaluate the response quality, as LLMs directly generate results rather than probability distributions, unable to calculate PPL.\n| Model            |BLEU@1| BLEU@4    |   Acc   |    Dist-1      |      Dist-2      |          \n|------------------|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|\n| DialoGPT  [1]   | 33.5 |11.4|  30.59   | 0.46  |    3.02  |   \n| ChatGPT  [2]   |**45.4**| **14.5**|  35.71 |  0.58 |   3.34   |  \n| **Ours-Soft**  |43.8|13.7| 42.57|0.68 | 3.38 | \n| **Ours-Hard**  |44.2|14.2| **42.59**|**0.72** | **3.49** | \n\n**1). Result analysis:**\nThe results are shown in Table 1. Our model outperforms DialoGPT in all performance, with higher emotion accuracy than ChatGPT, but still falls short of ChatGPT in response quality, which is basically consistent with the results of manual evaluation. It should be noted that the metric BLEU doesn’t fully reflect the gap between our model and ChatGPT in generation quality, as ChatGPT without fine-tuning generates high-quality responses but not similar to ground-truth responses. This also indicates that current evaluation metrics based on ground-truth comparison, cannot objectively measure the effectiveness of LLMs. \n\n**2). Summary of Advantages of our model:**\n\nTable 2: Comparisons of parameter quantity and generation speed (per sample) with few-shot prompting LLMs.\n| Model            |    parameters    | generation speed      |     \n|------------------|:-----------:|:-----------:|\n| DialoGPT  [1]  |   345M  |0.675s   |\n| ChatGPT  [2]  |175B |1.328s| \n| **Ours-Soft**  |**36.1M** |**0.034s**|\n| **Ours-Hard** | **36.1M** |0.036s|\n\n**<1>Fewer parameters and higher generation speed**: As shown in Table 2, our model achieves better performance than DialoGPT with  1/10 parameters (36.1M vs 345M), only inferior to ChatGPT (175B). For the generation speed, our model respectively achieves 19.8x and 39.1x speedup compared to online DialoGPT and ChatGPT.\n\n**<2>More accurate emotion prediction**: Our model achieves a more accurate emotion prediction compared to ChatGPT and DialoGPT (Acc: 42.59% vs 35.71%&30.59%). We observe that LLMs as the universal models, are inadequate in distinguishing similar emotions such as angry and annoyed. And our model with emotion correlation learning is good at the fine-grained emotion perception in the empathetic dialogue generation task.\n\n**<3>Strong scalability**: Our model has stronger scalability, as can be combined with other pre-trained models for joint training to achieve better performance. As in Tabel 6, our model trained with pre-trained language model DialoGPT shows excellent response performance (BLEU-1:0.624, Dist-1:3.07 Dist-2:4.92) and emotion accuracy (Acc: 50.12%).\n\nWe will supplement all the results and analysis to the revision as an extension.\n\n----\n**Attached the prompt used for few-shot prompting LLMs comparison:**\n\n1）For response generation:\n\nYour task is to generate a response in a consistent way.\\\n<context>: S1:I went through some of my old stuff yesterday, and I found my security blanket that I used when I was a kid!\\\n<response>: Awww I bet that brought back memories.\\\n<context>: S1:My husband is the most faithful man. L1:That is great to hear! A faithful spouse is a blessing.S2:I have so many health problems and he is always there for many no matter what being loving and caring.\\\n<response>: I am sorry to hear about that! I hope everything gets better for you!\\\n<context>: xxxx\n\n2）For emotion perception:\n\nPlease choose one words from the follwing set to describe above sentence in a consistent way. set: surprised, excited, annoyed, proud, angry, sad, grateful, lonely, impressed, afraid, disgusted, confident, terrified, hopeful, anxious, disappointed, joyful, prepared, guilty, furious, nostalgic, jealous, anticipating, embarrassed, content, devastated, sentimental, caring, trusting, ashamed, apprehensive, faithful. \\\n<context>: S1:I went through some of my old stuff yesterday, and I found my security blanket that I used when I was a kid!\\\n<emotion>: sentimental\\\n<context>: S1:My husband is the most faithful man. L1:That is great to hear! A faithful spouse is a blessing.S2:I have so many health problems and he is always there for many no matter what being loving and caring.\\\n<emotion>: faithful\\\n<context>: xxxx\n\n    \n\n**Reference:**\n\n[1] Dialogpt: Large-scale generative pre-training for conversational response generation, ACL, 2020.\n\n[2] https://openai.com/blog/chatgpt. Chatgpt, OpenAI, 2022.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1.\tThe authors propose a very new perspective to break the emotion independence assumption existing in current empathetic dialogue generation methods and model the intrinsic emotion correlation.\n2.\tThe experimental results show the usefulness of the proposed method in different evaluation methods.\n", "reasons_to_reject": "1.\tIt would be clearer to give a description of the overall process in the figure caption or the beginning of section 3 based on the contents of Figure 3.\n2.\tAre all emotion nodes connected to each other for the graphs of all instances?\n3.\tI like the motivation of emotion correlation, but the implementation of the overall method is a little bit complicated. Do you compare your method with directly few-shot prompting LLMs? What are the advantages of your methods and what are the drawbacks of the current LLMs when performing empathetic response generation?\n", "questions_to_authors": "1.\tAre all emotion nodes connected to each other for the graphs of all instances?\n2.\tI like the motivation of emotion correlation, but the implementation of the overall method is a little bit complicated. Do you compare your method with directly few-shot prompting LLMs? What are the advantages of your methods and what are the drawbacks of the current LLMs when performing empathetic response generation?\n", "paper_topic_and_main_contributions": "Previous works adopt an independent assumption on different emotions by first predicting emotion labels and then generating responses. This paper considers the co-occurrence of multiple emotions and proposes to mine and incorporate this intrinsic emotion correlation into single-labeled empathetic dialogue generation datasets.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "N/A", "Typos_Grammar_Style_and_Presentation_Improvements": "N/A"}, "extra_text": {}}, {"review_id": "WMUisWNNk6", "reviewer": "Reviewer_rPyr", "summary": "", "questions": "1) It’s not clear whether an emotion can be deemed as significant if its original score is not high. The original score of “afraid” in Fig. 1 is lower than that of “terrified”. It puzzles me how to make “afraid” surpass “terrified” after combining with a correlation weight matrix. Additionally, how to capture the “afraid” emotion in this dialogue? It seems no emotion-related words regarding “afraid” can be found.\n\n2) Why to model emotion correlations in a directed graph? Emotions are mutually related, and it’s hard for me to comprehend the afraid-to-terrified weight is different from the terrified-to-afraid weight.\n\n3) “Sorry to hear” is claimed as an unsuitable response in Fig. 1, while it is also generated by the proposed method. \n\n4) How to initialize the re-parameter matrix S?\n\n5) Some notations have not been formally introduced, e.g., e_w, e_p, and e_d in Eq. (1), A_ij and E_ij in Eq. (7), etc.\n\n6) In human evaluation, it’s unclear why E-CORE can achieve best under the metrics of fluency and relevance. It seems E-CORE pays less attention on fluency and relevancy learning in its response generation.", "limitations": "", "rating": 3, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer rPyr,\n\nThanks for your constructive review. We are encouraged that you found our paper to be well organized and see the potential of our method in the dialogue generation field. We provide detailed answers to your comments next.\n\n    \n----\n> **R1:** The motivation seems not clear.\n\nThank you for your concerns. The motivation is that existing studies ignore the intrinsic emotion correlation in dialogues, resulting in inaccurate emotion perception and inappropriate response generation (details in questions 1 and 3). As the psychology studies suggest an intrinsic correlation in human emotions, we propose to break the emotion independence assumption existing in current methods, and explicitly mine and incorporate the emotion correlation to enhance emotion perception and response generation. \n\n----\n> **R2:** Mathematical expressions should be more rigorous; \n> **& Q5:** Some notations have not been formally introduced, e.g., e_w, e_p, and e_d in Eq. (1), A_ij and E_ij in Eq. (7), etc.\n\nWe will carefully check all mathematical expressions as you suggested, to ensure the rigor of each expression and notation.\nAnd the notations in Eq. (1) and (7) have been introduced. In Eq. (1), e_w, e_p, and e_d respectively indicate three kinds of embeddings: word embedding, position embedding and dialog state embedding, as stated in lines 204-206. In Eq. (7), A_ ij indicates the calculated attention score of node i to neighboring node j (defined in Eq. (6)), E_ ij indicates the edge weight from node i to node j (the initialization process is defined in Eq.(3), and the update process is defined in Eq.(5)).\nWe will put these definitions closer to the equations, to make the expressions clearer. \n\n\n----\n> **R3:** Some experimental results have not been well explained;\n> **& Q6:** In human evaluation, it’s unclear why E-CORE can achieve best under the metrics of fluency and relevance. It seems E-CORE pays less attention on fluency and relevancy learning in its response generation.\n\nWe will first elaborate on the conclusions supported by existing experimental results. With the comparisons with SOTAs, we have demonstrated the promise of emotion correlation learning in both emotion perception (8.34% in Acc) and empathetic generation (8.53% in PPL,16.7% in Dist-2). With the ablation studies and visualization analysis, we further validated the essence of emotion correlation learning in our method E-CORE.\n\nFollowing your suggestions, we will further check all experiments and analyses to improve the explanation. Specific:\n**For the fluency metric**, all models show great performance and our model only make a  slight improvement compared to SOTAs, as the language models based on deep neural network are generally sufficient to generate fluent sentences.\n**For the relevancy metric**, our E-CORE significantly outperforms the SOTAs, as the emotion correlation learning helps provide more relevant emotions guidance, yielding empathetic responses rich in relevant emotions and semantics. The qualitative analyses (section 5.3) also confirm the conclusion, that our model generates more relevant and detailed phrases “good that you took them in”, “go back memories”, etc., while the SOTA models only produce universal responses.\n\n----\n> **Q1** It’s not clear whether an emotion can be deemed as significant if its original score is not high. The original score of “afraid” in Fig. 1 is lower than that of “terrified”. It puzzles me how to make “afraid” surpass “terrified” after combining with a correlation weight matrix. Additionally, how to capture the “afraid” emotion in this dialogue? It seems no emotion-related words regarding “afraid” can be found.\n\nThanks for this concern. We will first conduct a comprehensive analysis based on the visualization results Figure 5&6, and then answer your questions in turn.\n\nFirstly, the model captured the original dialogue emotion scores by fusing the attention weights of all dialogue words to each emotion, among which the words “terrified”, “hit” and “drunk driver” contribute to emotions “terrified” and “afraid”, and the words “glad” and “alive” contribute to “grateful” (Figure 5). Then a correlation-aware reweighting and aggregation is conducted on original scores, to obtain the final emotion scores. With a greater correlation weight of emotion “grateful” for “afraid” than for “terrified”, “afraid” showed greater score than “terrified” after aggregation (Figure 6). Thus:\n\n1)**Whether an emotion is deemed as significant does not only depend on its original score.** As the final emotion scores are determined by both original score and emotion correlation weights, **an emotion with a low original score but strongly correlated to other high-score emotions, may also have higher final scores.** (For example, in the case of case study, emotion “nostalgic” is deemed as significant, due to the strong correlation between “sentimental” and “nostalgic”)\n\n2)As in Figure 6, **“afraid” surpasses “terrified”, due to the greater correlation weight of emotion “grateful” for afraid than for terrified.**\n\n3)As in Figure 5, **the emotion “afraid” is captured with related words “terrified”, “hit” and “drunk driver”.**\n\nA detailed visualization analysis for the case of Figure 1 also has been provided in Appendix J. We will enhance the expression in the main text to provide clearer explanations.\n\n----\n> **Q2** Why to model emotion correlations in a directed graph? Emotions are mutually related, and it’s hard for me to comprehend the afraid-to-terrified weight is different from the terrified-to-afraid weight.\n\nThe emotion nodes are interconnected, with a symmetric matrix as the edge weights, so the emotion connection in the graph **is equivalent to an undirected graph**. The afraid-to-terrified weight **is the same as** the terrified-to-afraid weight.\nHowever, considering the directed connection of word nodes, the emotion graph is represented as a directed graph, for consistency in computation and representation.\nThanks for the reminder, we will clarify the explanations to avoid misunderstandings.\n\n----\n> **Q3** “Sorry to hear” is claimed as an unsuitable response in Fig. 1, while it is also generated by the proposed method.\n\nIn the case of Figure 1, the speaker first expressed the **afraid for accident**, and then transited to the **grateful for survival**. \nFor empathetic response generation, a suitable response should respond to the transited emotion, which is more in line with the speaker's expectations and communication habits.\nThus, we explain that only replying with 'sorry to hear' is not empathetic enough, while our proposed method replied both 'sorry to hear' and 'good that you look them in', which is a comprehensive response for the accident (afraid ) and survival (grateful).\nThanks for the reminder. We will further strengthen the expression to clarify the difference between the responses to avoid misunderstandings.\n\n\n----\n> **Q4** How to initialize the re-parameter matrix S?\n\nThe re-parameter matrix S is a learnable parameter that is **randomly** initialized during the initial training process, and then continuously updated along with model training.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1) The paper is mostly well organized and easy to follow;\n\n2) The study is interesting and may inspire the dialogue generation field;\n\n3) The visual demonstrations are impressive, as well as the intuitive examples in Table 9 and Table 10.", "reasons_to_reject": "1) The motivation seems not clear;\n\n2) Mathematical expressions should be more rigorous;\n\n3) Some experimental results have not been well explained.", "questions_to_authors": "1) It’s not clear whether an emotion can be deemed as significant if its original score is not high. The original score of “afraid” in Fig. 1 is lower than that of “terrified”. It puzzles me how to make “afraid” surpass “terrified” after combining with a correlation weight matrix. Additionally, how to capture the “afraid” emotion in this dialogue? It seems no emotion-related words regarding “afraid” can be found.\n\n2) Why to model emotion correlations in a directed graph? Emotions are mutually related, and it’s hard for me to comprehend the afraid-to-terrified weight is different from the terrified-to-afraid weight.\n\n3) “Sorry to hear” is claimed as an unsuitable response in Fig. 1, while it is also generated by the proposed method. \n\n4) How to initialize the re-parameter matrix S?\n\n5) Some notations have not been formally introduced, e.g., e_w, e_p, and e_d in Eq. (1), A_ij and E_ij in Eq. (7), etc.\n\n6) In human evaluation, it’s unclear why E-CORE can achieve best under the metrics of fluency and relevance. It seems E-CORE pays less attention on fluency and relevancy learning in its response generation.", "paper_topic_and_main_contributions": "A novel empathetic dialogue generation framework based on emotion correlation enhancement is proposed in this paper. A multi-resolution emotion graph is constructed to capture subtle emotion interactions, and an emotion enhanced decoder is devised for emotion perception and response generation.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: All reviewers appreciate the soundness of the work although they disagree about excitement, I think the question really revolves around the impact of modeling emotionally correlation in dialogue. I think this speaks to rPyr point about motivation. 

It is somewhat concerning that the details of the human evaluations are not stated in the appendix nor do there seem to be tests of statistical significance. Given the large superiority I do not see this is a fatal flaw, but something that the authors must include.
Decision: Accept-Main