CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation
Year: 2023

Abstract:
Empathetic conversation is psychologically supposed to be the result of conscious alignment and interaction between the cognition and affection of empathy. However, existing empathetic dialogue models usually consider only the affective aspect or treat cognition and affection in isolation, which limits the capability of empathetic response generation. In this work, we propose the CASE model for empathetic dialogue generation. It first builds upon a commonsense cognition graph and an emotional concept graph and then aligns the user’s cognition and affection at both the coarse-grained and fine-grained levels. Through automatic and manual evaluation, we demonstrate that CASE outperforms state-of-the-art baselines of empathetic dialogues and can generate more empathetic and informative responses.1

heading: 1 Introduction
text: Human empathetic conversations allow both parties to understand each other’s experiences and feelings (Keskin, 2014), which is crucial for establishing seamless relationships (Zech and Rimé, 2005) and is also integral to building a trustful conversational AI (Huang et al., 2020; Wang et al., 2021).
In social psychology, empathy consists of two aspects: cognition and affection (Davis, 1983). The cognitive aspect corresponding to the understanding of the user’s situation and experiences (Cuff et al., 2016). The affective aspect requires the comprehension of the user’s emotional state and his/her potential emotional reaction (Elliott et al., 2018). Although existing work of empathetic dialogue involves both aspects of empathy, there are still issues that need to be addressed. First, most work (Rashkin et al., 2019; Lin et al., 2019; Majumder
∗Work done during internship at the CoAI Group. †Equal contribution. ‡Corresponding author.
1The project repository is available at https://github. com/jfzhouyoo/CASE
et al., 2020; Li et al., 2020, 2022) considers only the affective aspect, like detecting the user’s emotional state to enhance empathy expression. Second, although recent work explored both roles of cognition and affection in empathy expression (Zheng et al., 2021a; Sabour et al., 2022), they usually treat cognition and affection in isolation without considering their relationship.
However, human empathetic responses often result from conscious alignment and interaction between cognition and affection of empathy (Westbrook et al., 2011). For one thing, the user’s overall emotional state manifested in the context suggests the user’s attitude toward current situation (i.e., cognition). Thus, for the listener, aligning the user’s expressed cognition to the proper emotional state is essential for an appropriate empathetic response. As in case-1 of Figure 1, the alignment of cognition (i.e., intent “to go to the beach”) with different emo-
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tional states (i.e., “excited” vs. “disappointed”) produces different appropriate empathetic expressions (i.e., “love” and “which beach are you going to go to” vs. “hate” and “waiting for the beach”), respectively. For another, the user’s situation drives the listener to infer the deeper specific cognitions and associate them with the underlying emotional reactions. In this way, the listener can produce a more actively empathetic response instead of only understanding and repeating the user’s expressed cognition. As in case-2 of Figure 1, building association between inferred cognitions and emotional reactions, i.e., “to give up” and “frustrated” vs. “to try harder” and “hopeful”, yields cognitively distinct but highly empathetic responses, i.e., response-2a vs. response-2b. The two cases highlight the necessity of aligning cognition and affection on both overall and specific (i.e., coarse and fine-grained) level for empathy modeling in response generation.
To this end, we align Cognition and Affection for reSponding Empathetically (CASE) on coarse and fine-grained levels by fusing sentence-level commonsense knowledge from COMET (Bosselut et al., 2019) and word-level concept knowledge from ConceptNet (Speer et al., 2017). Commonsense knowledge infers the user’s situation as cognition and infers emotional reactions to the situation, which are implied in the dialogue. Concept knowledge serves to extract the emotional state manifested in the dialogue. For encoding the two types of knowledge, we first construct commonsense cognition graph and emotional concept graph, where the initial independent representation of cognition and emotional concept is carefully adjusted by dialogue context adopting graph transformers. Then, we design a two-level strategy to align cognition and affection using mutual information maximization (MIM) (Appendix A) (Hjelm et al., 2019). The coarse-grained level considers overall cognition and affection manifested in the dialogue context to align contextual cognition and contextual emotional state, which are extracted with a knowledge discernment mechanism. The fine-grained level builds the fine-grained association between cognition and affection implied in the dialogue to align each specific cognition and corresponding emotional reaction. Further, an empathy-aware decoder is devised for generating empathetic expressions.
Our contributions are summarized as follows:
(1) We devise a unified framework to model the interaction between cognition and affection for in-
tegrated empathetic response generation. (2) We construct two heterogeneous graphs involving commonsense and concept knowledge to aid in the modeling of cognition and affection.
(3) We propose a two-level strategy to align coarse-grained and fine-grained cognition and affection adopting mutual information maximization.
(4) Extensive experiments demonstrate the superior of CASE in automatic and manual evaluation.

heading: 2 Related Work

heading: 2.1 Emotional & Empathetic Conversation
text: Emotional conversation gives the manually specified label preset as the emotion generated in the response (Zhou et al., 2018; Wei et al., 2019; Peng et al., 2022). Instead of giving a predefined emotion label, empathetic conversation (Chen et al., 2022; Kim et al., 2022) involves cognitive and affective empathy (Davis, 1983) and aims to fully understand the interlocutor’s situation and feelings and respond empathically (Keskin, 2014; Zheng et al., 2021b). For one thing, most existing works only focus on the affective aspect of empathy and make efforts to detect contextual emotion (Rashkin et al., 2019; Lin et al., 2019; Majumder et al., 2020; Li et al., 2020, 2022) while ignoring the cognitive aspect. For another, some research utilizes commonsense as cognition to refine empathetic considerations (Sabour et al., 2022). However, the relatively independent modeling between the two aspects (i.e., cognition and affection) violates their interrelated characteristics.

heading: 2.2 Commonsense & Concept Knowledge
text: As a commonsense knowledge base, ATOMIC (Sap et al., 2019) focuses on inferential knowledge organized as typed if-then relations. Six commonsense reasoning relations are defined for the person involved in an event, four of which are used to reason commonsense cognitions of a given event, i.e., PersonX’s intent before the event (xIntent), what PersonX need to do before the event (xNeed), what PersonX want after the event (xWant), and the effect of the event on PersonX (xEffect). Each commonsense cognition is aligned with user’s emotional reaction to the situation implied in the dialogue inferred by xReact (i.e., PersonX’s reaction to the event) in our approach. To obtain inferential commonsense knowledge, we use COMET (Bosselut et al., 2019), a pretrained generative model, to generate rich commonsense statements.
Unlike commonsense knowledge that provides sentence-level commonsense expression, we adopt ConceptNet (Speer et al., 2017) as concept knowledge, which provides word-level human knowledge and is widely used in various NLP tasks (Zhang et al., 2020; Zhong et al., 2021; Zhou et al., 2021; Yang et al., 2022). Following Li et al. (2022), we use NRC_VAD (Mohammad, 2018) to assign emotion intensity to concepts in ConceptNet (processing details are in Li et al. (2022)) severed to extract the contextual emotional state manifested in the context, and align it with contextual cognition.

heading: 3 Approach
text: CASE framework is in Fig. 2. The dialogue context X = [x1, . . . , xN ] contains N utterances, where xi denotes the i-th utterance. CASE contains three stages: (1) The graph encoding stage constructs and encodes heterogeneous commonsense cognition graph GCS and emotional concept graph GEC from the dialogue context X . (2) The coarse-to-fine alignment aligns coarse-grained (between contextual cognition and contextual emotional state) and fine-grained (between each specific cognition and corresponding emotional reaction) cognition and affection adopting MIM. (3) The empathy-aware decoder integrates the aligned cognition and affection to generate the response Y = [y1, y2, . . . , yM ] with empathetic and informative expressions.

heading: 3.1 Graph Encoding
text: Commonsense Cognition Graph Construction Given the last utterance xN of the dialogue context X , we segment it into the sub-utterances
U = [u0, u1, u2, . . . , ut], where we prepend the whole xN as u0 for maintaining the global information of xN . We use COMET to infer l commonsense cognition knowledge Kri = [kri,1, k r i,2, . . . , k r i,l] for each ui ∈ U , where r is one of the four commonsense relations R = {xIntent, xNeed, xWant, xEffect}, similar to Sabour et al. (2022). The idea is that human responses tend to inherit the above and transfer the topic. There are differences in the topic and connotation of different sub-utterances affecting the listeners’ concerns when responding empathetically.
For constructing the heterogeneous commonsense cognition graph GCS , we use the utterance set U and the commonsense cognition knowledge set KCS = ⋃t i=0 ⋃ r∈RK r i as vertices, i.e., vertex set VCS = U ∪KCS . There are seven relations of undirected edges that connect vertices. (1) The selfloop relation for each vertex vCSi ∈ VCS . (2) The global relation between the whole xN (i.e., u0) and its sub-utterances ui(i ≥ 1). (3) The temporary relation between any two successive sub-utterances uj and uj+1. (4) The four commonsense relations, i.e., xIntent, xNeed, xWant, xEffect, between the utterance ui ∈ U and the corresponding Kri .
We use a Transformer-based sentence encoder (cognition encoder) to first encode the vertices VCS of the graph GCS . For each vCSi ∈ VCS , we prepend with a special token [CLS]. Following Devlin et al. (2019), we collect the [CLS] representation as the initial embedding matrix for GCS .
Emotional Concept Graph Construction We concatenate the utterances in the dialogue context X to obtain the token set T , i.e., T = x1 ⊕ . . .⊕
xN = [w1, . . . , wn], where n is the number of all the tokens in the utterances in X . Following Li et al. (2022), we use ConceptNet to infer the related concepts for each token wi ∈ T , among which only the the top N ′ emotional concepts (according to the emotion intensity ω(c)) are used for constructing GEC . Subsequently, the vertices VEC in the heterogeneous emotional concept graph GEC contains a [CLS] token, the dialogue context tokens T , and the above obtained emotional concepts. There are four relations of undirected edges that connect vertices. (1) The self-loop relation for each vertex vECi ∈ VEC . (2) The global relation between the [CLS] token and other ones. (3) The temporary relation between any two successive tokens. (4) The emotional concept relation among a token and its related emotional concepts.
We initialize the vertex embedding for GEC by summing up the token embedding, the positional embedding, and the type embedding for each vertex (signaling whether it is a emotional concept or not).
Graph Encoder Given the commonsense cognition graph GCS , to capture the semantic relationship between vertices, we adopt the RelationEnhanced Graph Transformer (Li et al., 2021) for graph encoding. It employs a relation-enhanced multi-head attention mechanism (MHA) to encode vertex embedding v̂vi for vertex vi (we omit the superscripts CS for simplicity) as:
v̂vi = MHAvk∈VCS ( qvi ,kvk ,vvk ) , (1)
where the semantic relations between vertices are injected into the query and key vectors:
qvi = vvi + lvi→vk , kvk = vvk + lvk→vi , (2)
where lvi→vk and lvk→vi are learnable relation embeddings between vertices vi and vk. The selfattention is subsequently followed by a residual connection and a feed-forward layer, as done in the standard Transformer encoder (Vaswani et al., 2017). Finally, we obtain the commonsense cognition embedding csi for each vCSi ∈ VCS .
To encode the emotional concept graph GEC , we adopt a vanilla Graph Transformer (i.e., omitting the relation enhancement part in the above Graph Transformer). By superimposing the emotion intensity of each token, we obtain the emotional concept embedding eci for each vECi ∈ VEC .

heading: 3.2 Coarse-to-Fine Alignment
text: Context Encoding Following previous works (Majumder et al., 2020; Sabour et al., 2022), we
concatenate all the utterances in the dialogue context X and prepend with a [CLS] token: [CLS]⊕ x1⊕ . . .⊕xN . This sequence is fed into a standard Transformer encoder (context encoder) to obtain the representation SX of the dialogue context. We denote the representation of [CLS] as sX .
Coarse-grained Alignment To reproduce the interaction of cognition and affection manifested in the dialogue context, we align contextual cognition and contextual emotional state at an overall level. They are separately acquired by cognitive and emotional knowledge discernment mechanisms, which select golden-like knowledge guided by response.
To obtain the contextual cognitive representation rcog, the knowledge discernment calculates the prior cognitive distribution PCS (csi | X) over the commonsense cognition knowledge (that is, only KCS rather than all the vertices VCS in GCS , and we thus use 1 ≤ i ≤ |KCS | for simplicity):
rcog =
|KCS |∑
i=1
PCS (csi | X) · csi, (3)
PCS (csi | X) = softmaxi(csTi φCS (sX)), (4)
where φCS(·) is a MLP layer activated by tanh. Similarly, we calculate the prior emotional distribution PEC (eci | X) (1 ≤ i ≤ |VEC |) and obtain the contextual emotional representation remo.
During training, we use the ground truth response Y to guide the learning of knowledge discernment mechanisms. We feed Y into the cognition encoder (used for initializing the embeddings of GCS above) and the context encoder to get the hidden states ScogY and S ctx Y , where the [CLS] representations are sctxY and s cog Y respectively. The posterior cognitive distribution PCS (csi | Y ) and the emotional one PEC (eci | Y ) are calculated as:
PCS (csi | Y ) = softmaxi ( csTi s cog Y ) , (5) PEC (eci | Y ) = softmaxi ( ecTi s ctx Y ) . (6)
We then optimize the KL divergence between the prior and posterior distributions during training:
LKL = L CS KL + L EC KL, (7)
LCSKL =
|KCS |∑
i=1
PCS (csi | Y ) · log PCS (csi | Y ) PCS (csi | X) ,
LECKL =
|VEC |∑
i=1
PCS (eci | Y ) · log PEC (eci | Y ) PEC (eci | X) .
To further ensure the accuracy of discerned knowledge, similar to Bai et al. (2021), we employ the BOW loss to force the relevancy between cognitive / emotional knowledge and the target response. The BOW loss LBOW is defined as:
LBOW = − 1 |B| ∑
yt∈B log η(yt | r′cog, r′emo), (8)
where η(·) is a MLP layer followed by softmax and the output dimension is the vocabulary size, B denotes the word bags of Y , r′cog =
∑|KCS | i=1 PCS (csi | Y ) · csi, and r′emo =∑|VEC |
i=1 PEC (eci | Y ) · eci. Finally, we align the coarse-grained representations of the contextual cognition rcog and the contextual emotional state remo using mutual information maximization (MIM). Specifically, we adopt the binary cross-entropy (BCE) loss Lcoarse as the mutual information estimator that maximizes the mutual information between rcog and remo:
Lcoarse = 2fcoarse (rcog, remo)
− log ∑
r̃emo
exp(fcoarse(rcog, r̃emo))
− log ∑
r̃cog
exp(fcoarse(r̃cog, remo)), (9)
where r̃emo and r̃cog are the encoded negative samples. fcoarse(·, ·) is a scoring function implemented with a bilinear layer activated by sigmoid function:
fcoarse (a, b) = σ ( aTW coarseb ) . (10)
Fine-grained Alignment To simulate the interaction of fine-grained cognition and affection implied in the dialogue during human express empathy, the fine-grained alignment builds the fine-grained association between each inferred specific cognition and corresponding emotional reaction.
For each ui ∈ U , we infer the commonsense knowledge about emotional reaction KxReacti =[ kxReacti,1 , . . . , k xReact i,l ] using COMET, which is regarded as the user’s possible emotional reaction to the current cognitive situation. Since kxReacti,j ∈ KxReacti is usually an emotion word (e.g., happy, sad), we concatenate KxReacti and feed it into the Transformer-based encoder (reaction encoder) to get the representation of the emotional reaction Heri . Similar to (Majumder et al., 2020) and (Sabour et al., 2022), we use the average pooling to represent the reaction sequence, i.e., heri =
Average (Heri ). To avoid over-alignment of outof-context emotional reaction with cognition, we inject contextual information into the representation of reaction. We first connect heri with the context representation SX at the token level, i.e., Seri [j] = SX [j]⊕heri . Then another Transformerbased encoder takes Seri as input and output the fused information Seri
′. We take the hidden representation of [CLS] in Seri
′ as the emotional reaction representation eri of ui.
Finally, we build the association between the inferred specific cognition {⋃lj=1 csri,j} from ui for r ∈ R = {xIntent, xNeed, xWant, xEffect} and the emotional reaction eri using MIM. Recall that{⋃t
i=0 ⋃ r∈R ⋃l j=1 cs r i,j } exactly correspond to the commonsense cognition knowlege set KCS . The fine-grained BCE Loss Lfine is defined as:
Lfine =
t∑
i=0
∑
r∈R
l∑
j=1
[ 2ffine ( csri,j , eri )
− log ∑
ẽri
exp ( ffine ( csri,j , ẽri ))
− log ∑
c̃sri,j
exp ( ffine ( c̃sri,j , eri )) ] , (11)
where ẽri and c̃s r i,j are the encoded negative samples. ffine(·, ·) is implemented as: ffine (a, b) = σ ( aTW fineb ) . (12)
Altogether, the coarse-to-fine alignment module can be jointly optimized by Lalign loss:
Lalign = LBOW+LKL+Lcoarse+αLfine, (13)
where α is a hyper-parameter.
Emotion Prediction We fuse the contextual emotional state and emotional reaction to distill the affective representation, where we use er0 as the distillation signal of emotional reaction. This is because er0 is derived from the speaker’s last utterance and represents the overall emotional reaction. A gating mechanism is designed to capture affective representation raff :
raff = µ · remo + (1− µ) · er0, (14) µ = σ ( wTaff [remo; er0] ) . (15)
We project raff to predict the user’s emotion e:
Pemo(e) = softmax (W emoraff ) , (16)
which is supervised by the ground truth emotion label e∗ using the cross-entropy loss:
Lemo = − logPemo (e∗) . (17)

heading: 3.3 Empathy-aware Response Generation
text: We employ a Transformer-based decoder to generate the response. To improve empathy perception in response generation, we devise two strategies to fuse the two parts of empathy (i.e., cognition and affection). First, we concatenate the cognitive and affective signals rcog and raff with the dialogue context representation SX at the token level, which is then processed by a MLP layer activated by ReLU to integrate cognition and affection into the dialogue context:
S′X [i] = MLP (SX [i]⊕ rcog ⊕ raff ) . (18)
Second, we modify the original Transformer decoder layer by adding two new cross-attention to integrate commonsense cognition knowledge KCS = {csi}|KCS |i=1 and emotional concept knowledge KEC = {eci}|VEC |i=1 , which are inserted between the self-attention and cross-attention for S′X . The decoder then predicts the next token ym given the previously decoded tokens y<m, as done in the standard Transformer decoder. We use the negative log-likelihood loss Lgen to optimize the decoder:
Lgen = − M∑
m=1
logP (ym | X,GCS ,GEC , y<m) .
(19) Finally, we jointly optimize the alignment loss, emotion prediction loss, generation loss, and diversity loss proposed by Sabour et al. (2022) as: L = γ1Lalign+γ2Lemo+γ3Lgen+γ4Ldiv, where γ1, γ2, γ3 and γ4 are hyper-parameters.

heading: 4 Experiments

heading: 4.1 Experimental Setup
text: Dataset The experiments are conducted on the widely used EMPATHETICDIALOGUES (Rashkin et al., 2019) dataset, comprising 25k open domain conversations. In a conversation, the speaker confides personal experiences, and the listener infers the situation and emotion of the speaker and responds empathetically. Following Rashkin et al. (2019), we split the train/valid/test set by 8:1:1.
Baselines (1) Transformer (Vaswani et al., 2017): A vanilla Transformer-based response generation model. (2) Multi-TRS (Rashkin et al., 2019): A multi-task Transformer model that jointly optimizes response generation and emotion prediction. (3) MoEL (Lin et al., 2019): An empathy dialogue
model that combines the output of multiple decoders for generating. (4) MIME (Majumder et al., 2020): An empathy dialogue model that mimics the user’s emotion for responding. (5) EmpDG (Li et al., 2020): An empathy dialogue generator that utilizes multi-resolution user emotions and feedback. (6) KEMP (Li et al., 2022): A knowledgeaware empathy dialogue model that only uses concept knowledge. (7) CEM (Sabour et al., 2022): A commonsense-aware empathetic chatting machine that only exploits commonsense knowledge.
Implementation Details We implemented all models with Pytorch. We initialize the word embeddings with pretrained GloVE word vectors (Pennington et al., 2014). The dimensionality of embeddings is set to 300 for all corresponding modules. We set hyper-parameters l = 5, N ′ = 10, α = 0.2, γ1 = γ2 = γ3 = 1 and γ4 = 1.5. We use Adam optimizer (Kingma and Ba, 2015) with β1 = 0.9 and β2 = 0.98. The batch size is 16 and early stopping is adopted. The initial learning rate is set to 0.0001 and we varied it during training following Vaswani et al. (2017). The maximum decoding step is set to 30 during inference. All models are trained on a GPU-P100 machine. The training process of CASE is split into two phases. We first minimize LBOW for pretraining knowledge discernment mechanisms, and then minimize L for training overall model.

heading: 4.2 Automatic Evaluation
text: In the model’s generation evaluation, we adopt the widely used Perplexity (PPL) and Distinct-1/2 (Dist-1/2) (Li et al., 2016). Perplexity evaluates the general generation quality of a model. Distinct-1/2 evaluates the generated diversity by measuring the ratio of unique unigrams/bigrams in the response. In the model’s emotion classification evaluation, we measure the accuracy (Acc) of emotion prediction. Following KEMP and CEM, we do not report
word overlap-based automatic metrics (Liu et al., 2016), e.g., BLEU (Papineni et al., 2002).
In Table 1, our model outperforms all baselines and achieves a significant improvement on all metrics. First, our model achieves about 4.0% reduction on PPL compared to the best baseline, which shows that CASE is more likely to generate ground truth responses. Second, our model achieves 15.6% and 41.2% improvement on Dist1/2 compared to CEM, which indicates the superiority of CASE in generating informative responses at the unigrams and bigrams level. This is attributed to the coarse-to-fine alignment that allows CASE to inject more informative commonsense cognition on the premise of ensuring the perplexity of the generated response. Third, our model achieves about 17.9% and 7.8% improvement in prediction accuracy compared to KEMP and CEM, respectively. This verifies that CASE considers both aspects of affection (i.e., contextual emotional state and emotional reaction) more effectively than focusing only on a single aspect as KEMP and CEM.

heading: 4.3 Overall-to-Part Ablation Study
text: We conduct an overall-to-part ablation study in Table 2. In the overall ablation, first, we remove the commonsense cognition graph and emotional concept graph, called “w/o Graph”. The emotion prediction accuracy decreases significantly, which indicates that the two heterogeneous graphs make remarkable contribution to detecting emotion. Second, we remove the coarse-to-fine alignment, called “w/o Align”. The diversity of generation decreases significantly and emotion prediction accuracy drops distinctly. It supports our motivation that the alignment of cognition and affection leads to informative and highly empathetic expression.
In the part ablation, first, we remove two graphs, called “w/o CSGraph” and “w/o ECGraph”, respectively. From the results, we find that the contribution of the commonsense cognition graph is mainly
to improve the diversity of generation (i.e., Dist1/2), while the role of the emotional concept graph is mainly located in the recognition of emotion (i.e., Acc). This also supports our constructed motivation. Second, we remove coarse-grained and fine-grained alignments, called “w/o CGAlign” and “w/o FGAlign”, respectively. We observe that the alignment at the fine-grained level is more significant than the coarse-grained level in terms of overall contribution. This also matches our intuition that building the fine-grained association between cognition and affection is closer to the conscious interaction process during human express empathy.

heading: 4.4 Human Evaluation
text: Human Evaluation of CASE and Baselines Here, 200 contexts are randomly sampled and each context is associated with two responses generated from our CASE and baseline. Following Sabour et al. (2022), three crowdsourcing workers are asked to choose the better one (Win) from two responses by considering three aspects, respectively, i.e., (1) Coherence (Coh.): which model’s response is more fluent and context-related? (2) Empathy (Emp.): which model’s response expresses a better understanding of the user’s situation and feelings? (3) Informativeness (Inf.): which model’s response incorporates more information related to the context? We use the Fleiss’ kappa (κ) (Fleiss, 1971) to measure the inter-annotator agreement. As in Table 3, the results show that CASE outperforms three more competitive baselines on all three aspects. Especially, CASE outperforms baselines significantly in terms of empathy and informativeness, which shows the superior of modeling the interaction between cognition and affection of empathy, and supports the observations from automatic evaluation.
Human Evaluation on Variants of CASE To more intuitively verify the role of the key components of CASE in language expression, especially empathy ability, we conduct a scoring human evaluation for the variants of CASE. Besides the same settings as above, we require annotating the Overall preference score (1-5). As in Table 4, CASE achieves the highest scores in all aspects, indicating that all components contribute. The low empathy scores of “w/o Graph” and “w/o Align” as well as their variants further confirm the crucial role of graph structure and the effectiveness of alignment.

heading: 4.5 Applicability Analysis
text: To analyze the applicability of our method, we build it on the pre-trained model to explore whether it brings further benefits. We integrate BlenderBot (Roller et al., 2021) into CASE by replacing the encoder and decoder, and take the vanilla Bart (Lewis et al., 2020) and BlenderBot as baselines. All pre-trained models are small versions. As in Table 5, we found that CASE-BlenderBot integrating our method significantly outperforms finetune-only baselines. Although the overall performance of simple finetuning has achieved stage success, it is limited by the quality and scale of the dataset and lacks a more fine-grained design for the trait of human conversation. This also demonstrates the highlevel applicability of our method for uncovering the underlying mechanisms of human conversation.

heading: 4.6 Case Study
text: Two cases from six models are selected in Table 6, among which CASE is more likely to express informative cognition in a highly empathetic tone.
This is due to two main advantages: (1) Effective alignment between cognition and affection on two levels. For example, in the first case, on the fine-grained level, CASE associates the cognition “to be safe" with the affection “good” (i.e., emotional reaction) to appease the user’s “Terrified” experience, i.e., “to stay safe” and “get a little better”, in response. In the second case, on the coarse-grained level, in the user’s “Embarrassed” emotional state, CASE expresses empathetic affection “it is not too bad” with an informative cognitive statement, i.e., “get it fixed”, in response.
(2) Accurate identification of the conversational emotion integrating emotional concepts and reactions, being consistent with “Acc”. For instance, in the first case, the correct conversational emotion “Terrified” tends to be identified in the emotional concepts (“frighten, terrify, etc.”), while in the second case, the one “Embarrassed” tends to be identified in the emotional reactions (“embarrassed, ashamed, etc.”). Compared with baselines that cannot correctly perform two cases simultaneously, CASE identifies correct emotion in both cases by integrating emotional concepts and reactions.

heading: 5 Conclusion and Future Work
text: In this paper, for responding empathetically, we propose CASE to align cognition and affection by simulating their conscious interaction in human conversation. Extensive experiments verify the superiority of CASE on overall quality and empathy performance. Our work will also encourage future work to model the more complex interaction between cognition and affection in human conversation as well as other human language behaviors (Liu et al., 2021; Zheng et al., 2023).
Limitations
We discuss two limitations of this work as follows: One limitation of our work is the lack of taskspecific automatic metrics to evaluate the empathy of generated responses. Therefore, the evaluation of empathy relies more on human evaluation. Although human evaluation is a golden standard, automatic metrics help to conduct large-scale investigations. This is also a common limitation in current works on empathetic dialogue.
The second limitation is the passive response to the user’s cognition and affection. In many scenarios, empathy is used as a strategy for emotional support by responding to the user’s cognition and
affection. However, besides passive response, emotional support also requires active emotion elicitation, which can be studied in future work.
Ethical Considerations
In this paper, our experiments adopt the widely used EMPATHETICDIALOGUES benchmark, an open-source dataset collected from Amazon Mechanical Turk (MTurk) that does not contain personal information. We also ensure the anonymization of the human evaluation. We believe that this work honors the ethical code of ACL.

heading: Acknowledgements
text: This work was supported by the National Key Research and Development Program of China (No. 2021ZD0113304), the National Science Foundation for Distinguished Young Scholars (with No. 62125604) and the NSFC projects (Key project with No. 61936010).
This work was also supported by the National Natural Science Foundation of China (with No.

heading: 62272340, 61876128, 62276187).

heading: A Mutual Information Maximization
text: Mutual information maximization (MIM) aims to measure the dependence between two random variables X and Y , and the mutual information (MI) between them is defined as: MI(X,Y ) = DKL(P (X,Y )∥P (X)P (Y )). However, maximizing MI directly is normally intractable. A successful practice to estimate MI with a lower bound is InfoNCE (Kong et al., 2020). Given two different views x and y of an input, InfoNCE is defined by: EP (X,Y )[fθ(x, y)−EQ(Ỹ )[log ∑
ỹ∈Ỹ
exp fθ(x, ỹ)]]+log |Ỹ |,
(20) where fθ is a learnable function with parameter θ. The set Ỹ draws samples from a proposal distribution Q(Ỹ ), and it comprises |Ỹ | − 1 negative samples and a positive sample y. One insight is that when Ỹ always consists all values of Y and they are uniformly distributed, maximizing InfoNCE is analogous to maximize cross-entropy loss:
EP (X,Y )[fθ(x, y)− log ∑
ỹ∈Y exp fθ(x, ỹ)]. (21)
It shows InfoNCE is relevant to maximize Pθ(y|x) and approximates summation over elements in Y (i.e., partition function) by negative sampling (Zhou et al., 2020, 2022a,b). Upon the formula, we replace X and Y with specific cognition and affection to maximize MI between them.
ACL 2023 Responsible NLP Checklist

heading: A For every submission:

heading: 3 A1. Did you describe the limitations of your work?
text: Sec. Limitations

heading: 3 A2. Did you discuss any potential risks of your work?
text: Sec. Ethical Considerations

heading: 3 A3. Do the abstract and introduction summarize the paper’s main claims?
text: Abstract and Sec.1 Introduction
7 A4. Have you used AI writing assistants when working on this paper? Left blank.
B 3 Did you use or create scientific artifacts? Left blank.

heading: 3 B1. Did you cite the creators of artifacts you used?
text: Sec.2 Related Work and Sec.4.1 Experimental Setup
B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Not applicable. Left blank.

heading: 3 B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided
text: that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Sec.2 Related Work and Sec.4.1 Experimental Setup

heading: 3 B4. Did you discuss the steps taken to check whether the data that was collected / used contains any
text: information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Sec. Ethical Considerations
B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Not applicable. Left blank.
3 B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Sec. 4.1 Experimental Setup
C 3 Did you run computational experiments? Left blank.

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

heading: 3 C2. Did you discuss the experimental setup, including hyperparameter search and best-found
text: hyperparameter values? Appendix B Implementation Details

heading: 3 C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary
text: statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Sec.4 Experiments
C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Not applicable. Left blank.
D 3 Did you use human annotators (e.g., crowdworkers) or research with human participants? Left blank.

heading: 3 D1. Did you report the full text of instructions given to participants, including e.g., screenshots,
text: disclaimers of any risks to participants or annotators, etc.? Sec. 4 Experiments
7 D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants’ demographic (e.g., country of residence)? Limited by the space. Crowdsourcing workers are from Amazon Mechanical Turk.
D3. Did you discuss whether and how consent was obtained from people whose data you’re using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not applicable. Left blank.
D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. Left blank.
D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Not applicable. Left blank.

heading: Future Work
text: Our work will also encourage future work to model the more complex interaction between cognition and affection in human conversation as well as other human language behaviors (Liu et al. , 2021; Zheng et al. , 2023). Limitations
We discuss two limitations of this work as follows: One limitation of our work is the lack of taskspecific automatic metrics to evaluate the empathy of generated responses. Therefore, the evaluation of empathy relies more on human evaluation. Although human evaluation is a golden standard, automatic metrics help to conduct large-scale investigations. This is also a common limitation in current works on empathetic dialogue. The second limitation is the passive response to the user’s cognition and affection. In many scenarios, empathy is used as a strategy for emotional support by responding to the user’s cognition and
affection. However, besides passive response, emotional support also requires active emotion elicitation, which can be studied in future work. Ethical Considerations
In this paper, our experiments adopt the widely used EMPATHETICDIALOGUES benchmark, an open-source dataset collected from Amazon Mechanical Turk (MTurk) that does not contain personal information. We also ensure the anonymization of the human evaluation. We believe that this work honors the ethical code of ACL