Exploiting Emotion-Semantic Correlations for Empathetic Response Generation
Authors: Zhou Yang
Zhaochun Ren
Wang Yufeng
Xiaofei Zhu
Zhihao Chen
Tiecheng Cai
Wu Yunbing
Yisong Su
Sibo Ju
Xiangwen Liao
Venue: EMNLP 2023 Findings (2023)
Keywords: Emotion
Semantic
Correlation
Empathetic
Dialogue
Generation

Abstract: Empathetic response generation aims to generate empathetic responses by understanding the speaker's emotional feelings from the language of dialogue. Recent methods capture emotional words in the language of communicators and construct them as static vectors to perceive nuanced emotions.  However, linguistic research has shown that emotional words in language are dynamic and have correlations with other grammar semantic roles, i.e., words with semantic meanings, in grammar. Previous methods overlook these two characteristics, which easily lead to misunderstandings of emotions and neglect of key semantics.

To address this issue, we propose a dynamical Emotion-Semantic Correlation Model (ESCM) for empathetic dialogue generation tasks. ESCM constructs dynamic emotion-semantic vectors through the interaction of context and emotions. We introduce dependency trees to reflect the correlations between emotions and semantics. Based on dynamic emotion-semantic vectors and dependency trees, we propose a dynamic correlation graph convolutional network to guide the model in learning context meanings in dialogue and generating empathetic responses. Experimental results on the EMPATHETIC-DIALOGUES dataset show that ESCM understands semantics and emotions more accurately and expresses fluent and informative empathetic responses. Our analysis results also indicate that the correlations between emotions and semantics are frequently used in dialogues, which is of great significance for empathetic perception and expression.

Exploiting Emotion-Semantic Correlations for Empathetic Response Generation
Zhou Yang!,, Zhaochun Ren", Yufeng Wang Xiaofei Zhu', Zhihao Chen Tiecheng Cail?, Yunbing Wul Yisong Su Sibo Ju Xiinnwen Liao
'College of Computer and Data Science, Fuzhou University; DDiital Fujian Institute of Financial Big Data, Fuzhou, China *Leiden University, Leiden, The Netherlands
*College of Computer Science and Technology. Chongqing University of Technology, Chongqing. China
220311001, 21102'083, 0180320046, 210310002, 221010003, 221010003| e ezu..edu.. z z.rene iacs www.enenwi........mm.. zxf3cqut, cn 1wyb5820, 1iaox | fzu. edu.eddu.coom on
Abstract
Sentence 1: Ob well, there is always next time.
Empathetic response generation aims to gener- ate empathetic responses by understanding the speaker's emotional feelings from the language of dialogue. Recent methods capture emotional words in the language of communicators and construct them as static vectors to perceive nu- anced emotions. However, linguistic research has shown that emotional words in language are dynamic and have correlations with other grammar semantic roles, i.e., words with se- mantic meanings, in grammar. Previous meth- ods overlook these two characteristics, which easily lead misunderstandings of emotions and neglect of key semantics.
Will Mumartisccnstt FINET AivuE THmm,, Twam..
always (aor)
(1573)
UNIT
[
ABFAKD
nEXE [AD]]
Sentence 2: That is cingting when your team wins.
ISTRU
COF
COOUP
PIENTT
That
Coan
[RINETT
DE KSRR
Your [NET]
wins (YERS)
To address this issue, we propose a dy- namical -mootin--Sematiic Correlation Model (ESCM) for empathetic dialogue generation tasks. ESCM constructs dynamic emotion- semantic vectors through the interaction of context and emotions. We introduce de- pendency trees to reflect the correlations be- tween emotions and semantics. Based on dy- namic emotion-semantic vectors and depen- dency trees, we propose dynamic correlation graph convolutional network to guide the model in learning context meanings in dialogue and generating empathetic responses. Experimen- tal results on the EMPATHETIC-DIALOGE dataset show that ESCM understands semantics accurately and expresses
ADAMOO
when [ASK]
Figure 1:
Examples from EMPATHETIC-
DIALOGUES dataset. Sentence 1 shows the variability of emotional words. Sentence 2 shows the correlations of emotional words with semantic roles.
thetic responses by perceiving the speaker's emo- tional feelings (Rashkin et al., 2019; Zhong et al., 2021, 2020; Liang et al., 2021; Zheng et al., 2021; Liu et al., 2021; Wang et al., 2021).
Early methods perceive the speaker's feelings by understanding the holistically semantics and emotions expressed in the language of the context (Rashkin et al., 2019; Lin et al., 2019; Majumder et al., 2020). These methods are prone to gener- ate trivial and uninformed responses, ascribed to the neglect of nuances of human emotion in dia- logues (Li al., 2020). To address this issue, recent methods detect emotional words in the language of the communicators and build them as static vectors to perceive subtle emotions (Li et al., 2020; Sabour et al., 2022; Kim et al., 2021; Gao et al., 2021: Li et al., 2022; Kim et al., 2022). However, according to linguistic re-
and emotions fluent and informative empathetic responses. Our analysis results also indicate that the cor- relations between emotions and semantics frequently used in dialogues, which is of great significance for empathetic perception and ex- pression.
1 Introduction
Attracting an increasing amount of attention, empa- thetic response generation aims to generate empa-
"Corresponding author.
/Our code is available at https:
/ //git hub. nub. com/ 2houzhouyang520/
Empatheti cDi atg.atheeeciiiial.oeeeeeeeeeeee ogueeeneration ESCM

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search (Foolen al., 2012: Dirven, 1997; Osmond, 1997; Radden, 1998), focusing on emo- tional words while ignoring their characteristics in the expression process leads to emotional misunderstanddins and the neglect of words with important semantic information. As an important theory of emotional expression in linguistics, the conceptualization of emotions (Foolen et al., 2012) suggests that emotional words have two important characteristics in the expression process: variability and relevance. Variability that the affection of emotional words changes dynamically during the expression process. For example, "well" generally carries a positive meaning, but in sentence 1 (shown in Figure 1), it is used as an interjection to express neutral emotion. Using static vectors (such as Embedding (Pennington al., 2014; Mikolov et al., 2013) or VAD (Moham- mad, 2018)) to represent this dynamic emotion, previous methods are prone to misunderstand this sentence as positive.
tation and the dependency tree, ESCM proposes a dynamic correlation graph convolutional network to guide the model to capture the correlations be- tween emotions and semantics clearly. By learn- ing dynamic emotion-semantic representations and their correlations, ESCM accurately understands the emotions of the dialogue and captures important semantics to generate more empathetic responses.
We conduct experiments on the EMPATHETIC- DIALOGUES dataset (Rashkin et al., 2019). The results show that the ESCM model accurately un- derstands the dialogue and generates grammatically fluent and informative empathetic responses. Fur- thermore, we extract and statistically analyze the common correlation structures in dialogues from the Empathetic Empathetic-Diallguee dataset. The results in- dicate that the correlations between emotion and semantics are frequently and extensively utilized in expressing emotions during conversations. Ad- ditionally, the results of our analysis correlation structures are consistent with linguistic conclusions (Foolen et al., 2012).
Relevance refers to the grammatical correlations between emotional words and words carrying se- mantic meaning, which plays an important role in understanding emotions and semantics. For example, as shown in Figure 1, sentence 2 ex- presses the "exciting" emotion due to the victory of "team". "Team" is the primary subject described in the sentence, carrying key semantic information. Through the tion, the emotional word "exciting" directly modi- fies "team". Compared to previous work that did not consider such correlations, the model is more likely to identify key semantic words that are di- rectly associated with emotional words through relationships.
To sum up, our contributions are as follows:
We introduce the expressive characteristics of emotions in linguistics, including the variabil- ity of emotions and the correlations between emotions and semantics, to enhance the under- standing of the meaning in conversations.
correla-
We propose the ESCM model, which con- structs dynamic mmmtion-semantic vectors adjust the dynamics of emotions, and lever- ages a dependency tree- based dynamic corre- lation graph convolutional network to learn correlations, in order to generate empathetic responses.
these types of pyntax-meaninge Therefore, focusing on the variability and relevance of emotional words can promote the correct recog- nition of emotions and the detection of important semantics.
the EMPATHETIC- DIALOGUE dataset demonstrate the effectiveness of ESCM. Furthermore, addi- tional statistical and analytical experiments show that the correlations in dialogue are consistent with psychological research.
Experiments
Therefore, we propose a dynamical Emotion- Semantic Correlation Model (ESCM) for empa- thetic dialogue generation. ESCM dynamically constructs imotion-sseannee vectors through the interaction of context and emotions. By encod- ing emotion-semantii vectors, the model dynami- cally adjusts emotions and semanties in the context capture the variability of emotional words. To reflect the correlations between emotions and se- mantics clearly, we introduce a dependency tree. Based on the dynamic amotion-semantie represen-
Related Work
Empathetic response generation refers to empa- thetically responding by perceiving emotional feel- ings in the language of the speaker (Rashkin et al., 2019).
Early approaches explore the overall emotions of the conversation. Rashkin et al. (2019) intro-

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duce emotion representation generated by pre- trained emotion classifier learn and express spe-
3.2 Overview
We propose ESCM, which takes into account the dynamic emotions and semantics in the dialogue and their correlations. The proposed model is a tranformer-basee model with encoder-decoder ar- chitecture. To accurately perceive the content of the dialogue, we mainly reconstruct the encoder. As shown in Figure 2, ESCM mainly consists three parts: (1) context encoder (Section 3.3), which is a standard encoder structure and is used to understand the semantics of the dialogue; (2) dy- namic correlation encoding module (Section 3.4), which includes the construction of dynamic corre- lation vectors and the encoding of a dynamic cor- relation graph convolutional network. learns the correlations between emotions and semantics; (3) emotion and response predicting module (Section 3.5), which completes the functions of emotion prediction and response generation.
cific types of emotions in the conversation. How-
ever, emotions expressed in responses are often di- verse rather than specific (Lin et al., 2019). There- fore, Lin et al. (2019) utilize multiple professional emotion listeners to express various appropriate emotions. Majumder et al. (2020) group multi- ple conversation emotions by polarity and simulate the speaker's ' emotions to generate empathetic re- sponses.
These methods focus on the overall emotions of the conversation and ignore nuanced emotions (Li et al., 2020). To capture nuanced emotions, Li et al. (2020) extract emotional words through the NRC Emotion Lexicons (Mohammad and Turney, 2013) and integrate them into the model. Gao al. (2021) and Kim al. (2021) introduce emo- tional cause detection models to capture emotional words and perceive nuanced emotions. et al. (2022) enhance emotional representation the con- text with additional knowledge, which helps detect emotional words. Sabour et al. 2022 use com- monsense reasoning knowledge to infer nuanced emotions in the conversation. Kim et al. 2022 employ pre-trained models to detect word-level emotion and keywords detect the nuanced emo- tion in dialogues. These methods detect emotional words with nuanced emotions in the language of the conversation and use static vectors such as word embeddings or VAD to represent emotional words.
3.3 Context Encoder
As with previous methods (Li al., 2020, 2022; Sabour et al., 2022), we concatenate the utterances of the dialogue context and prepend [CLS] as the whole sequence token to form the context input C = == [CL[CL]] @ U1 @ U2 @ @ UM- Here, @ denotes the concatenation symbol. To input the context C' into the model, we convert C' into con- text word embeddings E.. Then, word embeddings E.,, position embeddings, and state embeddings to form the semantic embeddings Feg. The state embeddings are used to distinguish between speaker or responder types and are ran- domly initialized. To understand the semantics the dialogue, we feed the semantic embeddings E. into the context encoder Encat to obtain the context semantic representation Hş:
up the
Overall, early approaches ignore emotional words with nuanced emotions. Recent methods ig- nore two major characteristics of emotional words in linguistic expression: variability and correlation. Unlike these methods, consider the two charac- teristics of emotional words and propose dynamic mmmtaic-semantic correlation model to better un- derstand the conversation.
Har = EnCcet (Ee)
(1)
where Har E RLxd L is the length of the context sequence, and represents the hidden size the encoder.
3 Method
3.1 Task Formulation
3.4 Dynamic Correlation Encoding Module
Given a dialogue context D = [U1, .2.... UM of two interlocutors, model needs to accurately perceive the emotions and semantics in the dia- logue context and generate empathetic responses Y = Wy V2. ..../jj J. Here, U; = =[w{, wh ..., J represents the i-th utterance containing m, words. is a response containing N words.
Dynamic correlation encoding consists of two sub- modules: (1) Dynamic emotion-semantic vectors. brings the model the ability to flexibly adjust emotions and semantics, making the representation of context more reasonable. (2) Dynamic corre- lation graph convolutional network. This module

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Predict the hext Word
Predict the bootion Category
Decoder
Agerogation Attention
Emotion and Response Predicting Module
Aggregat Loe Acbennion
Dynamic Correlation
GCN Encoder
Demanic E-S Vectors Vese
Paotiion Eapedding
Dynamic Correlation Encoding Module
B-S Teeende
Guide
Depondency Tree
Context Encoder
Context Encoder
M3
W4
W5
W6
Context
An overview of ESCM. ESCM consists of three main key modules: (1) context encoder (Section 3.3), which encodes the semantic of context. (2) a dynamic correlation encoding module (Section 3.4), which learns the correlations between emotions semanties. (3) emotion and response predicting module (Section 3.5), which predicts dialog emotion categories and generates empathetic responses.
Figure 2:
uses emotions, semantics, part-of-speech, and de- pendency types to guide the model to discover and aggregate words with strong correlations, in order to more accurately understand the emotions and semantics of the conversation.
dings.
Edot (woEe (weEe + bo)
(3)
Ede = Weo + beo
(4)
where Wer bey Wer be Weer Dee are trainable parame- ters. Edst, Ege E Rlixde, and de is the number of emotional categories.
Dynamic Emotion-Semanti Vectors. Context has a significant impact on words, and understand- ing words without context may lead to errors. For example, without context, the word "well" is gen- erally considered to have a positive emotion, but in sentence 1 (shown in Figure 1), it functions as interjection to enhance the tone. This usage does not indicate positive mcaning. Therefore, it is necessary dynamically adjust emotions and semantics adapt the context.
We then feed the combined emotion and se- mantic vectors into the encoder to learn emotion- semantic representations. In this way, the model can take into account both emotion and semantics simultaneously during training to more comprehen- sively understand words in context.
Vdes = Ede ! Eds
(5)
Regarding semantics, we utilize weighted adjust- ments of context word embeddings with semantics.
H = (rncees(Vdes)
(6)
where Vdee. Encaes and Haes refer to the dynamic vectors, encoder, and representations for emotion- semantics, respectively. And Vdes. H C R plx((.+dd)
Eess = W. Frc +b
(2)
where W. and by are trainable parameters, and Eds E RLxd, d, is the hidden size of the dynamic semantic vector E,s--
Dynamic Correlation Graph Convolutional Network. The next problem is how to focus and learn grammatical correlations. Dependency trees clearly reflect the grammatical dependency relation- ships between related words (Kuncoro et al., 2016; Kiperwasser and Goldberg, 2016; Chen and Man- ning, 2014; Dozat and Manning, 2016). Therefore,
Regarding emotions, embeddings E. with emotion embeddings E, to obtain dynamic emotion vectors Ede- Emotion em- beddings refer to emotion categorics represented in word form, which are converted into vector embed-
interact context word

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we use dependency trees reflect the correlations between words. However, words with correlations in the dependency tree are not always important for understanding emotions and semantics. For ex- ample, "exciting" and "team" are more important, while "exciting" and "is" are relatively unimpor- tant. To distinguish these correlations, we consider multiple aspects of correlation guidance, including dynamic emotion-semantic representation, part-of- speech of related words, and the dependency types between them.
guiding vectors of node and node j. respectively, W. and by are trainable parameters, and Le is the ReLU activation function.
3.5 Emotion and Response Predicting
Based on the context semantics and the correlations between emotions and semantics, we predict the emotions of the conversation and generate empa- thetic responses.
Emotion Predicting. To understand the con- text semantics and capture important correlations, we use two aggregation networks with the same structure but different parameters to process the context semantic representation Hatz (Eq. 1) and correlation representation Herr (Liq. 9). We take the processing of the context representation example.
We list two examples to illustrate the validity of the above correlation guidance. In sentence (shown in Figure 1). the part-of-speech (ADJ and NOUN)² and the dependency type (CCOMP)) in- dicate that "exciting" is closely related to the key semantic "team". Conversely, the part-of-speech (ADJ and AUX)* and dependency type (COP)$ in- dicate that the correlation between "exciting" and "is" is trivial and unimportant.
We first calculate the weights of the words the context semantic representation Hetz and sum them up according to their weights to obtain the hidden layer representation H!2
Therefore, we consider the above multiple as- pects and concatenate the emotion-semantic repre- sentation, part-of-speech, and dependency type to form the guiding vector Vgl--
H Tanh Her + bb)
(10)
P. Softmaat (wn w H + bb))
(11)
VØ== = Háes + VV,
(7)
112 P.Pl Harli
(12)
where 1/p, Vr respectively denote ofrt-of-speech embeddings dependency type embeddings, which are randomly initialized. And Vp V, E [xx()))))) Vyt E tdd the embedding size of V, or V,,
where H1 E RLxd, P. E RIx1, H12 E Rd. wh 62, w., b) are learnable parameters, and Tank is the tanh activation function.
dyr is
Then we feed the hidden layer representation into a non-linear layer to learn and generate context semantic emotion probabilities Po
By assigning probabilities to each correlated neighbor, we aggregate the neighboring nodes that are associated in the dependency tree. Subse- quently, we obtain the correlation representation H
H = Tanh u ,12 + b2) pe Softmard (w? H2 + 63)
(13)
(14)
aij exp(p [ Vil
Pij Ej-1 Gis Car..rrpVV...... Vi Vo+li)
(8)
where H3 E Rd, Pa Rd. wa 02 w b2 are
learnable parameters.
Hoor ReLU
Pio (W.V Voeelil bb))
Similarly, we use the same structure to construct an aggregation attention network about the correla- tions and obtain emotion probabilities pe, E Rd We add the two types of emotion probabilities to- gether as the overall emotion probability P. Rd.
where Pijj RLXLx1, Heor E (Lx(d.+d.) aij is the value of the adjacency matrix about the de- pendency tree. When node and node have a direct relationship in the dependency tree, az is 1, otherwise it is O. V and Vy represent the
P. Pe + p
(15)
To ensure that important information in seman- tics and correlations is not affected by each other, we set loss functions for them separately. We em- ploy log-likelihood loss to optimize the parameters
PADJ: adjective. NOUN: noun
PCCMPP clausal complement
*AUX: auxiliary
ECOP: copula

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during the training phase based on the emotion category and the ground truth label.
EmpDG (Li al., 2020) emphasizes the impor- tance of user feedback and multi-resolution emo- tions. uses a generative adversarial network train the model and generate empathetic responses; KEMP (Li et al., 2022) employs ConceptNet as cxtra knowledge to enrich the representation of implicit emotions and captures these emotions to generate appropriate responses;
d = - log Perg (c)) f = Tog(Pan Poor(() (c"))
(16)
(17)
Response Predicting. Similarly, our decoder generates responses based on the context semantics and the correlations between emotion and seman- tics. This module takes the context semantic rep- resentation Har and the correlation representation Hoor as input and predicts the next word at each time step t. Similar to (Li et al., 2022), we use a point generator network to capture key vocabulary in the context and correlations.
CEM (Sabour et al., 2022) takes into account both the emotional and cognitive aspects of em- pathy. By incorporating reasoning knowledge, it enhances the ability to pereeive and express emo- tions.
4.2 Implementation Details
= Hat ① Heor
(18)
We conduct experiments on the EMPATHETIC- DIALOGUES (Rashkin et al., 2019) dataset. In the dataset, the number of emotions is dg=32. In the model, use Glove (Pennington al., 2014) the initialization vector for word embedding, with dimension of d=300. We set the dimension of the dynamic emotion vector to d,=10. At the same time, the dimensions the part-of-speech embed- ding and dependency type embedding are both set to =50 We use Biaffine Parser (Dozat and Man- ning, 2016) to obtain dependency relationships. For the multi-head attention networks in model, we 1 l-layer network and set the number of heads to 2. Subsequently, set the batch size to 16 and use the Adam optimizer (Kingma and Ba, 2014) optimize the parameters. After training for 13500 rounds on an NVIDIA Tesla T4 GPU, the model converged.
P(yrly < 1) DyctEyet H)
(19)
where H E Rlx(dida P(ully < t, () E RY.
V is the length of the vocabulary. Dec represents decoder with a pointer network.
Subsequently, we use cross-entropy genera- tion loss.
Cyon(ys) - Log(P(yrl < C)) (20)
Total Loss. Finally, add the loss ggen (y:) and two emotion losses Lag and feor together to obtain the total loss We optimize the training parameters in the model using the total loss.
[ C= (ye
Cete Lcor
(21)
4 Experiments
4.1 Baselines
4.3 Evaluation Metrics
We compare recent state-of-the-ar baselines with our model.
As with previous work, we employ both automatic and manual metrics to evaluate the performance of the model.
Transformer (Vaswani et al., 2017) is a vanilla Seq2Seq model, including both encoder and de- coder;
Automatic Evaluation Metrics. Following (Li et al., 2022: Sabour et al., 2022), we use the fol- lowing automatic metrics in our experiments: Per- plexity (PPL), Accuracy (Acc), Dist-1, and Dist-2. PPL measures language fluency, which is higher quality when the score is lower. Acc assesses the accuracy of emotion perception. Dist-1 and Dist- 2 (Li et al., 2015) measure response diversity at single and double granularity, respectively.
EmoPrend-1 (Rashkin et al., 2019) is Transformer-basee model that enhances empathy by incorporating emotion labels from pre-trained emotion classifier;
MoEL (Lin et al., 2019) is also Transformer- based model that softly combines various emotions with multiple decoders to generate empathetic re- sponses;
Human Evaluation Metries. Previous work scores models' responses on scale of 1 to 5 assess their quality (Li et al., 2020, 2022). This type of assessment prone to inconsistent results due to differences individual criteria (Sabour
MIME (Majumder et al., 2020) is Transformer- based model, which consider polarity- -based emo- tion clusters and emotional mimicry to generate appropriate responses;

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Models
Acc
PPL
Dist-1
Dist-2
Models
Acc
PPL
Dist-1
Dist-2
Transformer
37.73
0.47
2.04
ESCM
41.19
34.82
1.19
4.11
EmoPrend-1
33.28
38.30
0.46
2.08
w/o DESV
39.21
34.10
1.07
3.52
MoEL
32.00
38.04
0.44
2.10
w/o DCGCN
39.05
33.52
1.08
3.69
MIME
34.24
37.09
0.47
1.91
w/o V,
40.0
34.02
1.06
3.68
EmpDG
34.31
37.29
0.46
2.02
w/o V
39.41
34.45
0.99
3.33
KEMP
39.31
36.89
0.55
2.29
w/o Vdea
40.42
34.48
1.08
3.60
CEM
39.11
36.11
0.66
2.99
ESCM
41.19
34.82
1.19
4.11
Table 3: Results of the ablation experiments.
Table 1:
The automatic evaluation results.
MoEL, and MIME) are not as effective as mod- els that focus subtle emotions (EmpDG, KEMP, CEM). Additionally, we find that ESCM outper- forms the baselines all metrics. In terms of diver- sity, ESCM significantly outperforms the baselines. This suggests that focusing on the correlations be- tween emotions and semantics helps the model capture key semantics and express informative re- sponses. ESCM also outperforms the baselines in terms of emotion accuracy, indicating the effective- ness of the dynamic emotion-semenn
Comparisons
Aspects
Win
Lose
k
Emp.
45,4
24.0
0.48
ESCM
Rel.
52.8
16.3
0.43
EmpDG
Flu.
50.1
5.9
0.45
Emp.
44.0
20.0
0.57
ESCM
Rel.
53.3
21.0
0.46
VS. КEMP
Flu.
35.4
13.4
0.41
Emp.
37.3
19.8
0.58
ESCM CEM
Rel.
48.9
21.5
0.41
Flu.
33.8
11.6
0.47
vectors. Fur- thermore, ESCM achieves the best fluency, which indicates that the model combines emotions and semantics to express more natural language.
Table 2: Results of human evaluation. For more intu- itive display, we remove the result of the Tie and only show Win and Lose. Where is the inter-labeler agree- ment measured by Fleiss's kappa (Fleiss and Cohen, 1973), and 0.4 < S 0.6 indicates moderate agreement.
Human Evaluation Results. As shown in Table
2, ESCM outperforms the three strongest baselines in terms of empathy, relevance, and fluency. The superiority in empathy indicates that the model ac- curately understands and expresses emotions. The significant improvement in relevance suggests that the model captures and expresses key semanties. The superiority in fluency indicates that the model expresses more fluent responses by better under- standing the context.
et al., 2022). Therefore, we adopt the A/B test strategy (Lin et al., 2019; Majumder et al., 2020). Given two responses generated by the models for the same conversation, three professional erowd- sourcers are required to assign ESCM score of on Win when the response generated by ESCM is better than the compared model. Correspond- ingly, when ESCM better than or equal to the compared model, the crowdsourcers will add points for ESCM or Tie. Furthermore, three aspects are considered for evaluating models: empathy, rele- vance, and fluency. Empathy evaluates whether the responses show the right types of emotions; Rele- vance measures whether the reply is consistent with the theme and semantics of the context; Fluency as- sesses the response's 's readability and grammatical accuracy.
5.2 Ablation Studies
To verify the effectiveness of each component, the following experiments are conducted:
(1) w/o DESV: Dynamic emotion amotion-semantie semantic rep- resentations Hass (in Eq. 6) are replaced by context semantic representations Hete (in Eq. 1):
(2) w/o DCGCN: No dynamic correlation graph convolutional network (in Eqs. 7-9);
(3) w/o V.VVp/Vdes: Without the guidance vectors VytVV/VVes (in Eq. 7) in the dynamic corre- lation graph convolutional network.
5 Results and Analysis
The results of the ablation experiments are shown in Table 7. To verify the effectiveness of the dynamic emotion-semantic vectors, we remove DESV. The results show a significant decrease in emotional accuracy and diversity. The drop
5.1 Main Results
Automatic Evaluation Results. Table 1 shows the main results of the automatic evaluation for all models. We find that early models (EmoPrend-1,

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emotion accuracy suggests that dynamic emotion- semantic vectors play an important role in captur- ing emotions. The changes in diversity metrics demonstrate the crucial role of dynamic adjustment emotions and semantics in precise understanding and informative expression in conversations.
Models
T1 (s)
T2 (8)
Num
KEMP
0.17
4378.11
26,000
CEM
0.22
4438.68
20,000
ESCM
0.20
2733.94
13,500
Table 4: Results of time consumption. represents the average per-iteration time, T2 stands for the conver- gence time, and Num is the number of iterations needed convergence.
We remove DCGCN and its various guiding vectors to verify the effectiveness of the dynamic correlation graph convolutional network. After re- moving DCGCN, we find significant decrease in emotional accuracy and diversity. This indicates that correlations have a significant impact on the perception of emotions and semantics, and they play an important role in expressing informative responses. We further explore the role of various guiding vectors. When part-of-speech Vp or de- pendency types V,, are removed, the emotional ac- curacy decreases significantly. This indicates that ESCM can aggregate effective information related to emotions based on part-of-speech or dependency types. After removing part-of-speech dependency types, and emotion innotion-semantic vectors respectively. the diversity decreases significantly. This indicates that these features affect the aggregation of seman- tic information.
ModelsPPL Acc Dist-1
Dist-2
Size(G)
KEMP
39.31 36.89 0.55
2.29
6.02
CEM
39.11
36.11 0.66
2.99
5.57
VI
33.74 40.21 0.98
3.17
4.64
V2
34.82 41.63 1.07
3.52
6.00
V3
34.82 41.19 1.19
4.11
8.10
Table 5: Results under restricted resource consumption. 'Size' denotes the GPU memory required train the model. We list three ESCM models with different dpr values, where V1, V2, and V3 represent models with dp- 10, dpr 20, and dpr 50, respectively.
5.4 Time and Resource Consumption
To further demonstrate the efficacy of the model, conduct analytical experiments time and resource consumption.
Furthermore, find that removing any module improves the fluency of responses but decreases diversity. This mainly due to the fact that the ablation models express responses with more fluent yet less information, such as trivial sentences.
Time Consumption. As shown in Table 4, we calculate the time for our model and baselines to converge during training. The results show that our model does not have significantly higher average per-iteration time compared to the baselines. This mainly because: although the intermediate vari- able Vg intuitively has higher dimensionality. practice the dialogue context length short, so the impact on time is not substantial. Meanwhile, since our model can better understand the dialogue, it requires fewer iterations. Therefore, the total training time is actually significantly less than the baselines.
5.3 Correlation Analysis
To further explore the correlations, we con- duct a statistical analysis of the correlations in the EMPATHETTC-DIALOGGUU
dataset (see ap- pendix A for details). We extract 1138 correlations from the dialogue dataset, which used total of 151242 times in the dialogue. This indicates that the correlation structure frequently and repeat- edly used in the dialogue. In addition, we find that emotions are mainly expressed through three parts of speech: adjectives, nouns, and verbs. This is consistent with linguistic research (Foolen et al., 2012) on the parts of speech of emotional words. At the same time, also find that the "preposi- tion noun" structure is frequently used, which is also consistent with linguistic research (Foolen al., 2012). In each emotion type of empathetic dialogue, the frequently used correlation structures are similar, but the frequency of use may differ.
Resource Consumption. As shown in Table 5, we compute the GPU memory consumption of the models. Due to the higher dimensionality of the intermediate variable dpr in our model, our model requires slightly more resources than the basclines (see last row). However, even when reducing the GPU consumption of our model be comparable to the baselines, our model still significantly outper- forms the baselines overall (see penultimate row).

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Emotion Conext
Annoyed
between emotions and semanties. The proposed model constructs dynamic emotion-semantic vec- tor to reflect variability and uses a dependency tree- based dynamic correlation graph convolutional net- work to learn correlations. Both automatic and manual metrics demonstrate the effectiveness of the model. Furthermore, we conduct statistical analysis experiments. The results show that corre- lations frequently used in the dialogue. Addi- tionally, we find that the correlation structures the dialogue are consistent with linguistic research.
I hate when my little brother throws food the table just to annoy me
Gold
That terrible waste of food, how old is your little brother? I would be mad too
CEM KEMP
I am sorry to hear that. I hope you can get it back soon. That is really annoying, I hate when that happens.
ESCM
Emotion
Proud
To further investigate the correlation between emotion and semantics, we will take into account pre-trained knowledge, multilinguality, personal- ization, and other factors future work.
Conext
am constantly amazed by my sister's art skills. am not sure where she got them... my brother and I can not draw to our lives.
Limitations
Gold CEM
What does she draw?
That awesome! I bet she's great job!
The limitations of our work are follows: (1) Pre- trained models have become the mainstream nowa- days. To further explore the impact of pre-trained models, we constructed pre-trained ESCM model. Since the word coverage of the EMPATHETIC- DIALOGUES dataset in the vocabulary of pre- trained models is only 51.8%, we only surpassed the baseline Emp-RFi RFFT (Kim al., 2022) two metrics (Acc: 42.44 (42.08), Dist-2: 9.91 (4.48)). In the future, will further explore the impact of pre-trained models on correlations. lations between emotions and semantics discussed in this paper are only applicable to English. How- ever, different languages may have different types of correlations (Foolen et al., 2012). Therefore, we will investigate the correlations between emotions and semantics in multilingual contexts in the future. (3) Intuitively, there are individual differences in the expression of emotions. Due to data limita- tions, we did not consider this personalized factor. We will involve more research on correlations and personalization in the future.
KEMP ESCM
am sure you will do great!
That is really cool. bet you very proud of her.
Table 6: Case Study of ESCM and benchmarks. Words with Marked colors in the sentence are rich in emotion.
5.5 Case Study
(2) The corre-
As shown in Table 7, we select two strongest base- lines and compare them with ESCM through sam- ple analysis. In the first case, The speaker is an- noyed due to the fact that "the younger brother threw food onto the table". The baselines do not ac- curately understand the emotional expression "an- noyed" and the event described. However, ESCM understands and expresses "annoying" correctly, and gives a response indicating disgust towards the event. This indicates that ESCM is able to capture key semantics through correlations. In the second case, the speaker expresses the emotion of "proud" using the word "amazed" with surprised emotion. The baselines do not understand the emotions and semantics involved, while ESCM accurately under- stands the emotions and expresses an empathetic response. This demonstrates the effectiveness of building dynamic emotion-semantics.
Ethical Considerations
The potential ethical implications of our work are as follows: (1) Dataset: EMPATHETICDI- ALOGUES is an open-source, publicly available datasct for empathetic response generation. In the dataset, the original provider has filtered informa- tion about personal privacy and ethical implications
6 Conclusion and Future Work
This paper proposes ESCM, which introduces two characteristics of emotions in the linguistic ex- pression process: variability and the correlations
(Rashkin et al., 2019). (2) Models: Our baselines are also open source, and they have no permission issues. Since our model is trained on a healthy

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dataset, it does not generate discriminatory, abu- sive, or biased responses to users.
Eliyahu Kiperwasser and Yoav Goldberg, 2016. Simple and accurate dependency parsing using bidirectional Istm feature representations. Transactions of the As- sociation for Computational Linguistics, 4:313-327.
Acknowledgments
We would like to express our sincere gratitude to the reviewers for their diligent evaluation and con- structive feedback, which helped improve the qual- ity of this paper. In addition, we appreciate the insightful discussions and comments from the au- thors, which stimulated valuable thinking around this work. Their diverse perspectives and experi- ence shared through feedback have contributed im- mensely to the development of this research. This work was supported by National Natural Science Foundation of China (No.61976054).
Adhiguna Kuncoro, Miguel Ballesteros, Lingpeng Kong, Chris Dyer, Graham Neubig, and Noah A Smith. 2016. What do recurrent neural network grammars learn about syntax? arXiv preprint urXiv:1611.05774
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2015. A diversity-promoting objective function for neural conversation models. arXiv:abs/1510.03055.
Qintong Li, Hongshen Chen, Zhaochun Ren, Pengjie Ren, Zhaopeng Tu, and Zhumin Chen. 2020. Em- pdg: Multiresolution interactive empathetic dialogue generation. arXiv:abs/1911
References
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Danqi Chen and Christopher D Manning. 2014. fast. and accurate dependency parser using neural net- works. In Proceedings of the 2014 conference on empirical methods natural language processing (EMNLP), pages 740-750.
Yunlong Liang, Fandong Meng, Ying Zhang, Yufeng Chen, Jinan Xu, and Jie Zhou. 2021. Infusing multi- source knowledge with heterogencous graph neural network for emotional conversation generation. AAAI, volume 35, pages 13343-13352.
René Dirven. 1997. Esmotions as cause and the cause of emotions. The language of emotions, pages 55-84.
Timothy Dozat and Christopher D Manning, 2016. Deep biaffine attention for neural dependency pars- ing. arXiv preprint arXiv:161 1611::6 1.01734.
Zhaojiang Lin, Andrea Madotto, Jamin Shin, Peng Xu. and Pascale Fung. 2019. Moel: Mixture of empa- thetic listeners. In EMNLP-IJCNLP, page 121-132.
Joseph Fleiss and Jacob Cohen. 1973. The equiva-
Siyang Liu, Chujie Zheng, Orianna Demasi, Sahand Sabour, Yu Li, Zhou Yu, Yong Jiang, and Minlie Huang. 2021. Towards emotional support dialog systems. arXiv:abw2106.0111
Ience of weighted kappa and the intraclass correlation coefficient measures of reliability. Educational and psychological measwrement 33(3):613-619
Ad Foolen, Ulrike M Lüdtke, Timothy Racine, and Jor- dan 7latev. 2012. Moving ourselves, moving others:
Navonil Majumder, Pengfei Hong, Shanshan Peng, Jiankun Lu, Deepanway Ghosal, Alexander Gelbukh, Rada Mihalcea, and Soujanya Poria. 2020. Mime: Mimicking emotions for empathetic response genera- tion. In EMNLP, page 9968-89799
Motion and emotion intersubjectivity, conscious- ness and language. John Benjamins Publishing Com- pany.
Jun Gao, Yuhan Liu, Haolin Deng, Wei Wang, Yu Cao, Jiachen Du, and Ruifeng Xu. 2021. Improving em- pathetic response generation by recognizing emotion cause in conversations. In Findings of EMNLP, pages 807-819.
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Saif Mohammad. 2018. Obtaining reliable human rat- ings of valence, arousal, and dominance for 20,000 english words. In ACL, pages 174-184.
Hyunwoo Kim, Byeongehang Kim, and Gunhee Kim. 2021. Perspective-taking and pragmatics for generat- ing empathetic responses focused on emotion causes. In EMNLP.
Saif M Mohammad and Peter D Tumney. 2013. Crowd- sourcing a
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association lexicon. Com- putational intelligence, 2933)-3364465
Meredith Osmond. 1997. The prepositions we use the construal of emotion: Why do we say fed up with but sick and tired of. The language of emotions: Concep- Iualization, expression, and theoretical foundation. 21:111.
Diederik P Kingma and Jimmy Ba. 2014. Adam: method stochastic optimization. urXiv:abs/1412.600

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Correlations/Percentage
Examples joyful, scary time good felt proud into accident nice it
R00T-roo--ADJ--(7.22))
NOUN-amod--ADD--665551)
100
VERB-dep-ADD--b44.66)
ADP-pobj-NOUN--b(3.89
(Percentae)%%
80-
Camiidiion Others Emrdion Combetions
ADJ-nuubj-
60
Table 7: Top 5 correlations that are frequently used in conversations.
40
20
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014, Glove: Global vectors for word rep- resentation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532-1543.
01
10
30
30
60
80
100
Correlations of Frequendyy Used topk%
The percentage frequently used correla- tions the total number of correlations.
Figure 3:
Günter Radden. 1998. The conceptualisation of emo- tional causality by means of prepositional phrases. Speaking of emotions: Conceptualisation and expres- sion, pages 273-294.
shown in "All Correlation" in Figure 3 (the highest red line), for the overall correlations in the dataset, the top 10% of frequently used correlations account for more than 80% of the total number of correla- tions. That is, Crotat number of frequently used correlations in the top 10%, and Ctotal is the total number of correlations used in the dataset.
Hannah Rashkin, Eric Michael Smith, Margaret Li, and
Y-Lan Boureau. 2019. Towards empathetic open- domain conversation models: new benchmark and dataset. In ACI, page 5370-5381.
Cj =86. 17%, where C is the
Sahand Sabour, Chujie Zheng, and Minlie Huang. 2022. Cem: Commonsense-aware empathetic response gen- cration. In Proceedings of the AAAI Conference on Artificial Intelligence, Virginia, USA. AAAI Press.
(2) As shown in "Others: 32 Emotion Correla- tions" in Figure 3, for each type of emotional em- pathetic dialogue, the top 20% of frequently used correlations account for 80% of the total number of correlations. That is, C Catat
Ashish Vaswani, Neam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention all you need. NeurlPS, page 5998-6008.
Cj
approximately 80%.
Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng. Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma. and Hongan Wang. 2021. Cass: Towards building social-sun pocial-support chatbot for online health community. In PACMHCI, volume 5, pages 1-31.
For example, the number of empathetic dialogues expressing "joyful" emotion is Lotal =6083, and the number of frequently used associations the top 20% is Cyjouul
c:j0ffu_4945, =4945, so Cyouful /Ojoyful is approximately 81.13%.
Chujie Zheng, Yong Liu, Wei Chen, Yongcai Leng, and Minlie Huang. 2021. CoMAE: A multi factir hierar- chical framework empathetic response generation. In Findings of the Association for Computational Lin- guisties: ACL-IJCNLP 2021, pages 813 824, Online. Association for Computational Linguistics.
Part-of-Speech Phenomenon. As shown in Ta- ble we list examples of the most commonly used correlation structures. Taking "NOUN-anes -mmod-AD-- 6" as an example, "NOUN-amod-ADD" represents noun and an adjective linked together by the "amod" dependency type. "b" refers to the emotion word as the second word, and "I" indicates that the emotion word is the first word. Specifically, "ROOT" refers the root node of the dependency tree. In the frequently used correlations, emotions are mainly expressed through three parts speech: adjectives, nouns, and verbs, which is consistent with linguistic research (Foolen et al., 2012) on emotion words.
Peixiang Zhong, Wang. Pengfei Chen Zhang. Hao Wang, and Chunyan Miao. 2021. Care: Commonsense-awwe aware emotional response generation with latent concepts. In AAAI, volume 35, pages 14577-14585.
Peixiang Zhong, Chen Zhang, Hao Wang, Yong Liu, and Chunyan Miao. 2020. Towards pessona-based empa- thetic conversational models. arXiv:abs/2004.1 12316.
A Appendix
Frequency Phenomenon. As shown in Figure 3, we list the correlation statistical results. (1) As
Correlation Structure Phenomenon. As
shown in Table 7, we list the most commonly used

---

Type
Top1
Top2
8288)
R00T-1o0-ADD1-66-44))
NOUN-amod-ADD-N55.2)
excited(6471)
NOUN-amod-ADD--68.277
R00T-ro0--ADD-6(7..87
an2oyed(4210)
R00T-noo--DD1-b7..001
RO0T-root-VERB-N65.1
proud(5915)
R001-ro0--ADD-M11..7)
NOUN-amod-4DJJ-b65599)
angry(4859)
RO0T-roo--AD--((10.91)
ADI-asubj-PRON-t65999)
sad(3945)
NOUN-amoo(-ADDJ-677..255
RO0TT-roo-AADJ--b55665)
grateful(6487)
R00T-roo-ADJJ-b48.89)
NOUNNammod-4DD--666.99
lonely(3083)
1OUN-3amod-ADD-b111.3))
ADD-Pooj--NOUUN--66.99)
impres)ed(5483)
R00T-root-ADI-hh0.55))
NOUN-amood-AD1--77.55
afraid(52766
RO0T-no0--AD1--66.999
NOUN-amood-4DD-4444777
(isgusted(4322))
R00T-ro0-ADD--67.1199
VERB-deg--ADD-bb(5527)
confident(4203))
VERBB-ep-ADD--6(.55)
RO0TT-roof-ADD--b((-77))
(e3rificd(5436)
R00T-noo-AADJ-b65.998
NOUN-amod-44DD-644..77
hopeful(4635)
NOUN-amod-A1D1-5110.6666
RO0TT-roo--ADD--M7...2)
anxious(4946)
K001--00--AAD--b(6.00)
VEERB-dep-ADD--b662229
(isap))inted(3386)
NOUN-amod-ADD1-br..555
RO0T-root-AIDD-b666.81)
joyful(6083)
R00TTTroo-AAJ--b68.83)
NOUN-amodd-ADD--655555)
prepared(3617)
RO0T-rout-ADI-b66.999
ADP-Pooj--NOUUN-b66558
guilty(4979)
VERBB-dQ--AD--6122.0)
VERRB-acompp-AD---08444
furious(4978)
RO0T-root-ADI--b99.6))
ADJ-nsubh-PRRON-((447)
nostalgic(4012)
NOUN-amod-4DD--b99.211
RO0T--rot-AADD--(44.99)
jealous(5210)
NOUN-amod-AD11467..888
RO0T-root-ADD--b6..93)
anticipating(4502)
NOUN-amood-AD//466..995
R00T-root-ADDJ-bx6611))
cmbarraseed(3289
RO0T--ro--ADJ-b(55.11
VERB-dcp-ADD-bb44.65)
content(6632)
RO0TT-rot-AADJ--66..05)
NOUN-amood-ADJ-667.666
(3va5tated(3905)
ADP-Pobi-NNOUN--66.11)
RO0T-rrot-AADD--444.877
(331)))
NOUNaamod-ADD--b8.666)
R00TT-root-AID--666..66)
caring(4041)
RO0T-ro0-AAII-b66668))
NOUN-amod-ADJJ-66.557)
trusting(4193)
NOUNaamod-ADI--b6.55))
RO0T-root-ADD--66.339
(375m)
VERBB-ee-AADJ-b(0..55)
NOUN-amod-AADI-644.66)
apprehensive(4267) f313hful(32366
NOUN-amod-AD11466..999
ROOT-rrot-AAD--x7..55
NOUN-amoddADDJ-b88.00
RO0T--rot-AADJ-b(5587)
Table 8: Top frequently used correlations in conversations for each emotional dialogue. The numbers in the first
column indicate total number correlations for the emotional dialogue, while the numbers other columns represent the percentage of times the correlations are used.
correlation structures for each type emotion. also find that the "preposition + noun" method frequently used, which is consistent with linguistic research (Foolen et al., 2012).
As shown in Table 8, we list the most commonly used correlations in various emotional dialogues, with the numbers in parentheses indicating the probability of their usage. In each type of emo- tional empathetic dialogue, the frequently used cor- relation structures are similar, but the frequency of use may vary.


Reviews:
[{"review_id": "699zHtxvgQ", "reviewer": "Reviewer_BTHY", "summary": "", "questions": "1. Have you measured and calculated the computational consumption of the model? Analyzing this aspect can help refine the work.\n2. Have you explored the performance of the method when using PLM as a backbone?\n3. The method achieved good results on the metrics. With a better grasp of semantic, it is natural that ACC and PPL are better. And the model has a great improvement in DIST, even though there is no corresponding design. This is very interesting, have you analyzed what promotes DIST?", "limitations": "", "rating": 3, "confidence": 3, "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": "Thank you for your careful review and insightful critiques. We are grateful for the opportunity to respond to your comments point-by-point.\n\n**Model has additional modules and steps**\n\nEach module and step in our model contributes distinct improvements (as shown in Table 3 in the paper):\n- The dynamic emotion-semantics representations accurately adjust emotion and semantics for precision. Thus, ablating this causes declines in emotion accuracy and fluency. It also enables capturing salient keywords (see \"Reasons for improvements from DIST\"), resulting in the advantage in diversity.\n- The dynamic correlation encoding module captures direct relationships between adjacent nodes (words) in the syntactic dependency tree. Removing this decreases emotion accuracy and diversity, as it learns emotion through correlations and captures keywords from multiple angles (see \"Reasons for improvements from DIST\").\n- The emotion and response prediction modules predict emotion and generate responses. We use aggregated attention to focus on relevant dialog context to predict emotion categories. Without this module, the model cannot predict overall emotion contained in the context. For response generation, we solely use the decoder.\n- Additionally, ablating all modules increases fluency but decreases diversity, indicating the model produces more fluent but generic sentences, e.g. \"I am so sorry to hear that.\"\n\n\n**Some intermediate variables affect efficiency and scalability & Calculating computational consumption**\n\nThank you for raising this important concern. Regarding this issue, we first calculated the time consumption and GPU usage of our model, and found that the average iteration time is consistent with the baselines, while the overall convergence time is significantly shorter. For the issues of excessive GPU usage and difficulty in scaling up, we also propose two feasible and straightforward solutions. The detailed description is as follows:\n\nThrough observation and comparison, we found that the intermediate variable $V_{qk} \\in R^{L \\times L \\times (d_s + d_e + 2d_{pr})}$ in our model has higher dimensionality and requires more resources and time. To compare resource and time consumption, we selected KEMP and CEM as baselines and tested them under the same experimental conditions. The following are the experimental  results:\n\n**Results of resource and time consumption**:\n\n| Models | Average per-iteration time (s) | number of iterations for convergence | convergence time (s) |\n\n| --- | --- | --- | --- |  --- |\n\n| KEMP | 0.1763 | 26,000 | 4378.11 |\n\n| CEM | 0.2219 | 20,000 |  4438.68  |\n\n| ESCM (ours) |0.2025| 13,500 | 2733.94 |\n\n\n**Results under restricted resource consumption**：\n\n| Models | PPL | Acc | Dist-1 | Dist-2 | required GPU memory size (G) |\n\n| KEMP | 36.89 | 39.31 | 0.55 | 2.29 | 6.02 |\n\n| CEM | 36.11 | 39.11 | 0.66 | 2.99 | 5.57 |\n\n| ESCM($d_{pr}$=10) | 33.74 | 40.21 | 0.98 | 3.17 | 4.64 |\n\n| ESCM($d_{pr}$=20) | 34.82 | 41.63 | 1.07 | 3.52 | 6.00 |\n\n| ESCM($d_{pr}$=50) | 34.82 | 41.19 | 1.19 | 4.11 | 8.10 |\n\n\n- Time consumption. We calculated the time for our model (ESCM) and baselines to converge during training. The results show that our model does not have significantly higher average per-iteration time compared to the baselines. This is mainly because: although the intermediate variable $V_{qk} ∈ R^{L × L × (d_s + d_e + 2d_{pr})}$ intuitively has higher dimensionality (with L \\* L  \\* (10 + 2 \\* 50)), in practice the dialogue context length L is short, so the impact on time is not substantial. Meanwhile, since our model can better understand the dialogue, it requires fewer iterations. Therefore, the total training time is actually significantly less than the baselines.\n\n- Resource consumption. Due to the higher dimensionality of the intermediate variable $V_{qk} \\in R^{L \\times L \\times (d_s + d_e + 2d_{pr})}$ in our model, it requires slightly more resources than the baselines (see last row). However, even when reducing the GPU consumption of our model to be comparable to the baselines, our model still significantly outperforms the baselines overall (see penultimate row).\n\n- Enhancing efficiency and scalability. To reduce resource consumption, we constructed models with lower dimensionality in $d_{pr}$. Results show that appropriately reducing hyperparameters can still outperform baselines. Furthermore, larger dialogue context lengths $L$ also greatly increase model consumption. In the future, we will extend the model to a hierarchical model. The model will split long contexts into short sentences or paragraphs, and learn them using a HRED structure[2]. This structure can effectively resolve the issue of excessive resource consumption. After resolving the above resource consumption issues, our model will be better scalable.\n\n**Glove word vectors do not consider dynamic semantics**\n\nSince Glove word vectors are fixed, they indeed lack dynamic semantics. In our model, we adjust these vectors with weights, which can dynamically adjust the semantic vectors to some extent. We then combine the adjusted semantic vectors and dynamic emotion vectors, and input them into an encoder. Since the encoder can adjust and learn word representations according to context, this further amplifies the dynamic emotions and semantics. Therefore, our model has the capability of dynamically adjusting emotions and semantics.\n\n**Using PLM as a backbone to improve dynamic semantic encoding**\n\nThank you for your constructive suggestion. Since PLMs have strong capabilities in encoding dynamic semantics, we have constructed a variant model using BART. Due to page limit constraints, we did not include this model in the paper. However, we will add it in the revised paper. This model uses BART as our context encoder and emotion-semantics encoder. It can better encode dynamic semantics and emotions, while also demonstrating superior performance.\n\n**Reasons for improvements from DIST**\n\nAccording to previous methods [3], better attending to key words in the dialogue facilitates expressing informative and diverse responses. The improved diversity is because our model can capture important emotional and semantic words in the dialogue from multiple aspects. Specifically explained as follows:\n- First, our model dynamically adjusts emotions and semantics, making the representations of emotions and semantics more accurate, and key words easier to be captured by the model. \n- Second, our model is able to perceive part-of-speech, which can alleviate the influence of unimportant words to some extent, such as prepositions, auxiliaries. Meanwhile, it can also pay attention to more meaningful words, such as nouns and adjectives. \n- Third, the model can capture important words based on correlations. For example, in sentence 2 (\"That is exciting when your team wins\"), the word \"team\" with important semantics has direct correlation \"[ADJ]-CCOMP-[NOUN]\" with the emotional word \"exciting\" in the dependency syntax tree. Through such direct correlations, the model more easily captures important keywords. \n- In summary, the model is able to capture important words in the dialogues from multiple aspects, which facilitates generating diverse responses.  That is, if the model captures unimportant words (e.g. \"is\", \"that\"), it would be unable to generate complex, informative responses based on the captured terms. Instead, the model is more likely to produce generic, low diversity responses such as \"I'm sorry to hear that.\" \n\n\n[1] Foolen, Ad, et al. Moving ourselves, moving others: Motion and emotion in intersubjectivity, consciousness and language.\n\n[2] Serban, Iulian, et al. Building end-to-end dialogue systems using generative hierarchical neural network models.\n\n[3] Kim, Wongyu, et al. Emp-rft: Empathetic response generation via recognizing feature transitions between utterances.", "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 explore the correlations between emotions and semantics, this is a novel and significant area in empathetic response generation.\n2. The paper includes detailed experiments and comparisons with existing state-of-the-art models, providing insights into the effectiveness of the proposed approach.", "reasons_to_reject": "1. The method introduces many additional modules and steps, and some intermediate variables have higher dimensions. This may lead to significant computational overhead for the model, potentially affecting its efficiency and scalability.\n2. The paper employs Glove word vectors, which do not consider dynamic semantics. However, the current mainstream in NLP is Pre-trained Language Models (PLMs), which take into account the influence of context in language encoding. The paper does not discuss the application of the method under PLMs, which may limit the method's impact and practicality.", "questions_to_authors": "1. Have you measured and calculated the computational consumption of the model? Analyzing this aspect can help refine the work.\n2. Have you explored the performance of the method when using PLM as a backbone?\n3. The method achieved good results on the metrics. With a better grasp of semantic, it is natural that ACC and PPL are better. And the model has a great improvement in DIST, even though there is no corresponding design. This is very interesting, have you analyzed what promotes DIST?", "paper_topic_and_main_contributions": "The paper focus on empathetic response generation in natural language processing. It explores the correlations between emotions and semantics, and introduces a model called ESCM. The paper includes experiments conducted on the EMPATHETIC-DIALOGUES dataset and compares the proposed model with state-of-the-art baselines. It also discusses the limitations and ethical considerations of the work. The paper appears to be well-structured, covering various aspects of the research, including implementation details, evaluation metrics, and references to related works.", "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": {}}, {"review_id": "OdoP3SCWXb", "reviewer": "Reviewer_jjZg", "summary": "", "questions": "- how does the embedding compare to other affect-sensitive embeddings such as SentiX (Zhou et al., COLING 2020)?\n- will the code be released once the paper is published?", "limitations": "", "rating": 3, "confidence": 3, "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": "We appreciate you taking the time to provide such thoughtful and constructive feedback. We will address each of your points individually.\n\n**The first contribution has already been done in previous work (Foolen et al., 2012)**\n\nThank you for the opportunity to clarify the differences between the prior work ((Foolen et al., 2012)) and our current work. Here are three key distinctions:\n- Foolen et al.'s research was linguistic, discovering dynamic, correlative relationships between emotion and semantics in syntax.\n- Our work applies these emotion-semantic correlations to empathetic dialogue generation. We dynamically model emotions and semantics, and construct a dependency tree-based GCN to capture such relationships.\n- In summary, Foolen et al.'s work was linguistic, while we have applied their findings to empathetic dialogue generation. To further articulate our contributions, we will reframe our descriptions in the revised paper.\n\n\n**Improving the clarity of the paper's description (e.g. lines 78-83) & More intuitive instead of purely technical explanation of the system**\n\nWe have checked and revised the paper's descriptions to state the content more clearly. To clearly express the content in lines 78-83, we have revised the description. The original description has been changed to: \"Relevance refers to the grammatical correlations between emotional words and words carrying semantic meaning.\" In addition, for the model description section, we have modified the descriptions to make the model more intuitive rather than technical.\n\n**Comparing with the embedding of SentiX**\n\nOur model is based on a single-layer transformer, while SentiX is based on pre-trained models. Therefore, in order to compare the superiority of the two word embeddings in terms of metrics, we need to upgrade our model to a pre-trained model and conduct corresponding comparative experiments. Due to time constraints, we did not conduct such experiments, but chose to compare the two embeddings intuitively and methodologically.\n\n- Intuitively, SentiX is constructed to obtain domain-invariant sentiment knowledge. The sentiment embeddings in this model tend to be static vectors that do not change with domain. In contrast, our model aims to dynamically adjust vectors to obtain context-dependent sentiment representations. The sentiment vectors in our model are dynamic vectors. Therefore, intuitively they are complementary.\n- Methodologically, SentiX mainly optimizes sentiment word embeddings through masking techniques and loss functions, while our model mainly dynamically changes the representations of word embeddings. They optimize the model through different approaches. So from the methodological perspective, they are also not in conflict.\n- Overall, SentiX and our model can capture static and dynamic sentiments respectively, which are complementary ways that can be combined to further improve sentiment word representations.\n\n**Releasing the code**\n\nWe have organized the code and will release it as soon as possible after the paper is published.\n\n**Typos Grammar Style And Presentation Improvements**\n\nWe have carefully checked and revised issues related to expressions, including missing spaces before parentheses (e.g. parentheses on line 80 and 204), citation errors (e.g. wrong author citation on line 175), and spelling errors (e.g. \"Dynamitc Vecoter\" in Figure 2).\n\n[1] Foolen, Ad, et al. Moving ourselves, moving others: Motion and emotion in intersubjectivity, consciousness and language.", "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": "- the proposed approach is well-founded, based on an established work in linguistics\n- extensive experiments and evaluations\n- results show that the proposed approach is effective for the task", "reasons_to_reject": "- to my understanding the first contribution listed by the authors has been done in previous work (Foolen et al., 2012)\n- clarity of the paper can be improved. some explanations are difficult to understand due to convoluted language, e.g. lines 78-83, . Explanation of the system can be made more intuitive instead of purely technical.", "questions_to_authors": "- how does the embedding compare to other affect-sensitive embeddings such as SentiX (Zhou et al., COLING 2020)?\n- will the code be released once the paper is published?", "paper_topic_and_main_contributions": "This paper proposed to leverage emotion-semantic correlations for empathetic response generation. Existing works rely on static embedding to encapsulate the context and emotional information, whereas linguistic research suggests that the emotional meaning of word is dynamic and this is tied to semantic correlations and word roles in the sentence. Emotion-Semantic Correlation Model (ESCM) 1) proposes a dynamic emotion embedding, 2) identify semantic correlations and highlight ones that are important, and use this to guide the empathetic response generation. Experiments on the empathetic dialogue dataset shows improvements over a number of baselines. Ablation study shows that each of the proposed\n\ncontributions\n- a dynamic emotional embedding which considers contextual information\n- incorporation of dependency trees in emotion detection and empathetic response generation to highlight semantic correlations that are important for understanding emotion", "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": {"Typos_Grammar_Style_and_Presentation_Improvements": "the paper will benefit from thorough proof reading. there are a number of formatting errors such as missing space before beginning of parentheses, citation format error in line 175, typos in figure 2, etc."}}, {"review_id": "FbrdlFX4fj", "reviewer": "Reviewer_D6ne", "summary": "", "questions": "Given the identified issue, I have a strong feeling that it can be easily solved by using contextualised word embeddings. I wonder why no study in EDS ever uses LLM or even something like ELMo.", "limitations": "", "rating": 3, "confidence": 3, "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": "We greatly appreciate you dedicating time to provide such valuable feedback on our work. Your comments will help strengthen our paper.\n\n**The example is not persuasive**\n\nThank you for pointing out the issue. Thank you for catching this issue. Our original explanation was unclear. To clarify, we have reworded the example:\nExample 2 expresses the \"exciting\" emotion due to the victory of \"team\"'. \"Team\" is the primary subject described in the sentence, carrying key semantic information. Through the \"[ADJ]-CCOMP-[NOUN]\" correlation, the emotional word \"exciting\" directly modifies \"team.\" Compared to previous work that did not consider such correlations, the model is more likely to identify key semantic words that are directly associated with emotional words through these types of syntax-meaningful relationships.\n\n**Some terms are a bit confusing**\n\nThank you for pointing out this issue. Due to our oversight on our part, some descriptions were not clearly expressed. For example, in the sentence \"the neglect of key semantics\", \"key semantics\" should be \"words with important semantic information\". We have thoroughly re-checked the descriptions in the paper and revised any unclear expressions.\n\n**The baseline CEM, built 2 years ago, is post-powerful**\n\nThe model CEM [1] was formally published in AAAI 2022. Recent models [2][3][4] published in ACL 2023 all use it as the strongest baseline model. Therefore, it is reasonable to use CEM as the latest baseline.\n\n**A better baseline of Emp-RFT was not adopted**\n\nTo our knowledge, Emp-RFT [5] is a 6-layer transformer network based on a pre-trained model BART. Our model and baselines are single-layer networks, trained only on the EMPATHETIC-DIALOGUES dataset. It would be unfair to directly compare our model with Emp-RFT. But we can still demonstrate our effectiveness in the following two perspectives:\n- First, although our model has fewer parameters and is non-pretrained, some of its metrics (Dist-2: 4.11, Acc: 41.19) are still comparable to those of Emp-RFT (Dist-2: 4.59, Acc: 42.08).\n- Second, compared to existing baselines, our model is built only on dialogue context, yet it outperforms models with external knowledge (KEMP) [6] and commonsense reasoning knowledge (CEM). This further verifies the efficacy of our proposed approach.\n\nIn addition, to validate the efficacy of our method, we will also add BART-based variant models for comparison with Emp-RFT in the revised paper.\n\n**Contextualized word embeddings can effectively address dynamic emotions and semantics issues**\n\nModels with contextualized word embeddings (e.g. ELMo) can effectively understand dynamic semantics. However, they do not focus on the dynamic changes of emotions, and the impact of emotion-semantic correlations. In our model, we dynamically compute emotional intensity changes for words. Meanwhile, we also leverage GCN to capture dynamic emotion-semantic correlations in syntax. Therefore, we address the above issues from three aspects: dynamic emotions, dynamic semantics, and their correlations, rather than just focusing on dynamic semantics.\n\n\n**LLMs/PLMs have not been utilized in EDS**\n\nTo enhance model performance, some researchers have proposed several pre-trained models, such as Emp-RFT (NAACL 2022) [5] and several models published at the ACL 2023 conference[2][3][4]. Regarding the Emp-RFT model, we will introduce it in the revised paper and make further descriptions and comparisons. As for the models published at ACL 2023, since these models had not been published when EMNLP 2023 started soliciting papers, we did not introduce them.\n\n**Missing References of Emp-RFT**\n\nThank you for your constructive suggestion. To make a more comprehensive comparison and introduction of models for this task, we will add a citation of the Emp-RFT model in the revised paper.\n\n**Typos Grammar Style And Presentation Improvements**\n\nThank you for your suggestions. We have checked and revised issues with descriptions in the paper, such as the missing keyword \"Figure\" on line 81.\n\n[1] Sabour, Sahand, et al. Cem: Commonsense-aware empathetic response generation.\n\n[2] Cai, Hua, et al. Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge.\n\n[3] Zhou, Jinfeng, et al. CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation.\n\n[4] Bi, Guanqun, et al. DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation.\n\n[5] Kim, Wongyu, et al. Emp-rft: Empathetic response generation via recognizing feature transitions between utterances.\n\n[6] Li, Qintong, et al. Knowledge bridging for empathetic dialogue generation.", "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 identified issue (static word embeddings) is not only valid in the construction of EDS but also valid in the broader tasks that need emotion analysis.\n2. This the quite a complete research, in which the effectiveness of the solution was validated by both automatic evaluation and human evaluation.", "reasons_to_reject": "1. To motivate the study, this paper pointed out that the relations between emotional words and others (especially nouns) are vital for EDS, but the example here is not persuasive. Why knowing the relation is CCOMP is a piece of useful information for building EDS?\n2. The terms used in the paper are sometimes a bit confusing. For example, it appears to me the \"semantics\" in the sentence \"correlations between emotions and semantics\" and the sentence \"the neglect of key semantics\" have different meanings. Are emotion and semantics two exclusive concepts in your paper? \n3. EDS is a very popular topic these years. CEM the post-powerful baseline in this paper was actually built 2 years ago. A quick search on the more recent systems can easily find systems with better performance. For example, Emp-RFT reported better performance. It is better to also include more recent studies as such in discussion and comparison.\n\n ", "questions_to_authors": "Given the identified issue, I have a strong feeling that it can be easily solved by using contextualised word embeddings. I wonder why no study in EDS ever uses LLM or even something like ELMo.", "paper_topic_and_main_contributions": "The current paper first pointed out that previous research in Empathetic Dialogue systems (EDS) only captures emotional words as static vectors, which results in overlooking the pragmatics of these words and their correlations with other words. To address this problem, this paper introduces ESCM, which can construct dynamic emotion-semantic vectors. Some experiments suggest that the proposed model has promising performance.\n\n", "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": {"Missing_References": "Emp-RFT: Empathetic Response Generation via Recognizing Feature Transitions between Utterances (Kim et al., NAACL 2022)", "Typos_Grammar_Style_and_Presentation_Improvements": "shown in 1 -> shown in figure 1"}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper explores empathetic response generation in natural language processing, introducing the Emotion-Semantic Correlation Model (ESCM) to leverage dynamic emotion-semantic correlations. It conducts experiments on the EMPATHETIC-DIALOGUES dataset, comparing ESCM with state-of-the-art baselines and addressing ethical considerations.
Decision: Accept-Findings