Empathetic dialogue assembles emotion understanding, feeling projection, and appropriate response generation. Existing work for empathetic dialogue generation concentrates on the two-party conversation scenario. Multiparty dialogues, however, are pervasive in reality. Furthermore, emotion and sensibility are typically confused; a refined empathy analysis is needed for comprehending fragile and nuanced human feelings. We address these issues by proposing a novel task called MultiParty Empathetic Dialogue Generation in this study. Additionally, a Static-Dynamic model for Multi-Party Empathetic Dialogue Generation, SDMPED, is introduced as a baseline by exploring the static sensibility and dynamic emotion for the multi-party empathetic dialogue learning, the aspects that help SDMPED achieve the state-of-the-art performance. Empathetic conversation studies have been coming to the forefront in recent years owing to the increasing interest in dialogue systems. Empathetic dialogues not only provide dialogue partners with highly relevant contents but also project their feelings and convey a special emotion, that is, empathy. As revealed by previous studies (Fraser et al., 2018; Zhou et al., 2020), empathy can enhance conversation quality and transmit appropriate emotional responses to partners. Accordingly, most, if not all, existing work focuses on taking an emotional perspective in dialogue studies (Levinson et al., 2000; Kim et al., 2004; Bertero et al., 2016; Fraser et al., 2018; Rashkin et al., 2019). Although the empathetic conversation has received extensive attention, its exploration is still limited to the scenario with only two parties. In fact, multi-party chatting scenes are common in seminar discussions, conferences, and group chats. Multi-party conversations also rely on aid from empathy analysis. For instance, people with a similar experience can smoothly communicate with each other and easily feel understood, encouraged, and supported. These observations encourage us to present a novel natural language processing task called Multi-Party Empathetic Dialogue Generation. Generating multi-party empathetic dialogues faces two challenges. One challenge is the way to model multi-party dialogues. First, existing two-party dialogue models follow a seq2seq structure, whereas most multi-party dialogues are nonsequential. As shown in Figure 1, in response to Speaker 1 , the third and fourth utterances both express empathy for his/her stress and struggle. Sec-298 ond, in addition to the target participant, other participants also have implicit influence and interaction, and should be considered of generating utterances at each step. For instance, as an example of how to successfully resolve the situation, Speaker 4 inspires Speaker 1 as well as relieves Speaker 3 of his/her worry. Another challenge is the way to model the fragile and nuanced feelings of dialogue participants. We first clarify the relations of sensibility, emotion, and empathy in this study. Previous empathy studies recognized the emotion of one party and generated dialogues coupled with the same emotion (Rashkin et al., 2019; Shin et al., 2020). However, empathy is also determined by sensibility, which is a perspective-taking ability to experience other part-ners' emotions and make an appropriate response with his/her own view. According to the response I went through this in Figure 1, we can find that Speaker 4 has a similar experience to Speaker 1 , while Speaker 2 can only provide superficial comfort to Speaker 1 due to his/her weak sensibility. We observe that sensibility arises from personality and experience, and remains static throughout a conversation. On the other hand, emotion may dynamically change. For example, Speakers 2, 3 , and 4 possess different sensibilities to Speaker 1 , and these personal background-related attributes are persistent in the conversation. By contrast, the emotion of Speaker 1 gets reversed after receiving positive replies, as well as the main tone of this dialogue. To comprehensively cope with the aforementioned challenges in this study, we present a S taticD ynamic model for M ultiP arty E mpathetic D ialogue Generation called SDMPED. SDMPED models multi-party dialogues by constructing a dynamic graph network with temporal information and explores participants' dynamic emotions and static sensibilities by fusing speaker information. The contributions of our work are as follows:  We propose a new task called Multi-party Empathetic Dialogue Generation, which attempts to resolve the emotional changes and empathy generation of multiple participants in a conversation. We propose an effective baseline model SDMPED for this new task, which combines dynamic emotions and static sensibilities from multiple parties. We demonstrate that our approach leads to performance exceeding the state of the art when trained and evaluated on multi-party empathetic data. Considering empathy in modeled conversations has been proposed as early as 20 years ago (Levin-son et al., 2000). However, this idea has not been widely studied in NLP field due to the limitations of the available data. Recently, Rashkin et al. (2019) re-introduced the concept of empathetic dialogue and constructed the first empathetic dialogue dataset, EMPATHETICDIALOGUES (ED), which contains 32 emotions in 25K dialogues. Another dataset, PEC (Zhong et al., 2020), provides assurance that most of the data are in line with the characteristics of empathy, yet it lacks emotion-related annotations. Another limitation is that data in PEC come from only two forums on Reddit (i.e., happy5 and offmychest). The data in BlendedSkillTalk dataset (Smith et al., 2020) are collected from the ED, ConvAI2 (Dinan et al., 2020), and Persona-Chat (Zhang et al., 2018) datasets. However, only a small portion of these data are characterized by empathy. Notably, none of the aforementioned datasets have multiple (>2) persons participating in the same conversation, neither they include empathy degree labels. Shin et al. (2020) formulated a reinforcement learning problem to maximize the user's emotional perception of the generated responses. Li et al. (2020b) utilized the coarse-grained dialogue-level and the fine-grained token-level emotions, which helped better capture the nuances of user emotions. In Caire (Lin et al., 2020), the empathy generation tasks are reinforced with an auxiliary objective for emotion classification by using a transfer learning model. Nevertheless, current empathetic dialogue models are conducted in the context of two participants; they do not explore the implicit interactions among multiple speaking persons and do not consider the differences in their sensibilities. There have been quite a few studies on multi-party conversations before (Strauss and Minker, 2010), but they all focused on speech rather than conversational text. A recent multi-party study (Meng et al., 2018) has tended to focus on the Address and 299 Utterance u i s j Speaker Token Speaker Position Context Response    + Content t T+1 E m o t i o n Speaker 3: Gee,  achieve it! Response Selection (ARS) task and ignore the influence of emotions, which is a significant departure from our empathetic dialogue task. Over the last years, researchers have gradually shifted from studying simple emotions in two-party dialogues (Busso et al., 2008; Li et al., 2017) to conducting more complex emotion analysis of multiple participants. STAC (Asher et al., 2016) and ARS (Ouchi and Tsuboi, 2016) are the multiparty dialogue datasets without emotion labels. MELD (Poria et al., 2019) and MESID (Firdaus et al., 2020) create the multi-modal multi-party emotional dialogue datasets from the TV series Friends . However, these two datasets contain the emotion-related data derived from short and colloquial chats from TV series, and consequently, their dialogue quality cannot be guaranteed. Additionally, these datasets can only be utilized for simple upstream tasks, such as emotion recognition. Most of the dialogues in current datasets are daily conversations on trivial topics, while those modeling empathy dialogues are lacking. Majumder et al. (2019) proposed a conversational emotion recognition model based on RNN to dynamically model the states of multiple speakers. Later, Ghosal et al. (2019) and Li et al. (2020a) also studied context and speaker sensitivity based on the approach of Majumder et al. (2019). A common problem of these models is that they only focus on the accuracy of emotion recognition while ignoring the dynamic changes of emotions. In this section, we introduce a static-dynamic model called SDMPED as shown in Figure 2. We begin by describing the construction of the Temporal Dynamic Graph Network (TDGCN), including speaker sensibility nodes, emotion-related utterance nodes, and various types of edges between them. Thereafter, we use TDGCN to obtain dynamic emotions and static speaker sensibilities by integrating nodes and edges. Finally, we use prompt tuning to generate final dialogue responses based on emotion and sensibility information. We regard an empathetic post and its meaningful replies as a dialogue and ensure that each dialogue has more than three participating speakers. A post contains replies from multiple people, along with associated emotion and empathy degree labels. The empathy degree label of each utterance will be used in conjunction with the emotional content in our future model to learn the sensibility of each person. We propose a concept called dialogue emotional turn, which is different from the traditional dialogue turn. Specifically, a dialogue is assumed to have multiple sentences in one emotional turn but with the same emotional tone. When a person utters a second sentence, the emotion may already differ 300 from the previous one. Other people's subsequent utterances and emotions will be centered around this sentence. Therefore, we divide the dialogues to study the emotion variations over time, according to the principle that the same speaker can make at most one utterance during each emotional turn. Then, we introduce key symbols and concepts used in our study. A T emotional turns dialogue with N utterances between M ( M > 2) speakers can be expressed as U = { u ik | 1  i  N and 1  k  M } , where u ik represents the i th sentence from j th speaker. To better study emotion variations, we specify that a speaker can at most utter one sentence in each emotional turn. Thus, U can be divided into U = { U t | 1  t  T } , where each part U t has n t nodes. Further, the sensibilities of speakers can be expressed as S = { s 1 , s 2 , ..., s M } . Our model aims to generate an empathy response of length L . SDMPED captures the sensibility information and emotional variations of multiple parties owing to a novel graph network. First, we train the multi-scale TextCNN (Zhang and Wallace, 2015) according to the empathy degrees of our dataset, and we extract the d dimensional utterance-level features containing sensibility information. In each turn, we use the emotion of the first speaker as the main emotional tone, and extract the emotional content features based on those emotion labels in the same way. Using these sensibility-related features as nodes and speaker-utterance relationships as an adjacency matrix, we construct a two-step static graph network to determine the static sensibility information HS = { ( H x ) S | 1  x  M } of speakers. Thereafter, we represent the dialogue as a directed graph G = ( V, E, R, W ) to obtain additional emotional information. The graph is constructed as follows: Nodes V : The node set V = { v ik | 1  i  N and 1  k  M } incorporates emotion-related utterances. Among them, each node v ik (abbreviated as v i ) is initialized with the extracted feature u i spoken by the speaker s k . Adjacency Matrix E : E represents the adjacency matrix between emotion-related utterances. e ij  E represents the edge from the utterance node v i to v j . et al., 2019; Yang et al., 2021): the relative occurrence positions of u i and u j in the conversation (with three types of relations, namely, Before , Current , and After ) and both speakers of the constituting utterance nodes, as shown in Figure 3. Edge Weights W :Based on our assumptions, the edge weights are based on similarity-based attention, and the edge weights  ij  W are calculated as follows:  ij = softmax( u Ti W [ u i  p , ..., u i + f ]) , for j = i  p, ..., i + f. And the relationship between the utterance and its speakers  ki in static graph network can also be represented as c Freq . Speaking frequency of the speakers F req denotes the utterance number of a speaker in the whole conversations. c is a speaking coefficient to avoid over-fitting. Time Division Before feeding it into TDGCN, we need to divide E into T steps: E = { E t | 1  t  T } . At time step t , the divided matrix E t includes only edges corresponding to the utterance in the emotional turn t . As shown in Figure 1, four speakers participate in the dialogue with 7 utterances. This dialogue has two emotional turns: u 1 to u 4 and u 5 to u 7 . The nodes and edges are constructed in Figure 3. We take node u 3 as an example. The edge e 13 represents that u 1 spoken by s 1 appears before u 3 spoken by s 3 and the influence between them; the self-loop e 33 represents the influence of current node u 3 on itself. Two-Step Graph Update: The graph update mechanism has been implemented in two steps in order to better track conversation information and dynamic emotions. The update mechanism is calculated as follows: h (1) i =  ( (cid:80) r  R (cid:80) j  N ri  ij c i,r W (1) r u j +  ii W (1)0 u i ) , h (2) i =  ( (cid:80) j  N ri W (2) h (1) j + W (2)0 h (1) i ) , (1) where  ij and  ii are the edge weights and N ri denotes the neighboring indices of node v i under relation r  R . c i,r can be set in advance, such as c i,r = | N ri | . is the activation function ReLU, while W (1) r , W (1)0 , W (2) , and W (2)0 are learnable parameters. Utilizing the Two-Step Graph Update mechanism, we can effectively normalize the local neighborhood through neighborhood connections and enable self-dependent feature transformation through 301 The Relationship of Nodes and Edges u 3 u 4 u 1 u 2 s 1 s 3 , Before s 2 s 3 , Before s 4 s 3 , After s 3 s 3 , Current Sensibility Content + Emotion GRU GRUTDGCN t 1 t 2 EmotionTracker u 1 u 2 u 3 u 7 u 5 u 4 u 6 u 1 u 2 u 3 u 4 Figure 3: Transformation of dynamic emotions from t 1 to t 2 , as well as various types of edges between different nodes (e.g., Node u 3 ). self-connections, thereby extracting further information (Ghosal et al., 2019): We can call these two steps RGCONV and GCONV respectively in Figure 2. 3.3 TDGCN Previous dynamic graphs were mostly used in spatio-temporal traffic networks with separated spatial and time features (Guo et al., 2019; Zhao et al., 2020). However, given that the utterance node is time-related and changes frequently, we implement the dynamic graph by updating a weight matrix through GRU and updating the hidden layer through the two-step graph: M ( l ) t = GRU( H ( l ) t  1 , M ( l ) t  1 ) , H ( l ) t = GCONV(RGCONV( E t , H ( l ) t  1 , M ( l ) t )) , (2) where t  [1 , T ] and l  [1 , L ] ( L generally equals 2) denote the time and layer index, respectively. M ( l ) t  1 represents the weight matrix updated by GRU. H (0) t is equal to the node features V. The hidden state H ( l ) t of the l th layer at time step t can be divided into n t parts: H ( l ) t = { ( h x ) ( l ) t } , where x represents the speaker index. By concatenating person's sensibility with corresponding emotion-related content ( h x ) ( l ) t , we obtain dynamic emotion embedding: ( e x ) ( l ) t = (cid:104) ( H x ) S ; ( h x ) ( l ) t (cid:105) . P e = softmax ( W l e t +1 ) , L emo =  log ( P e [ e ]) . (4) 3.4 Decoder and Loss We adopt prompt tuning (Lester et al., 2021) to generate responses, which is a lightweight alternative to fine-tuning the generation task and keeps language model parameters unchanged while optimizing the prompt. The prompt adjustment achieves comparable performance in the full data setting by learning only parameters with a small proportion. The representation e t +1 is first transformed by a linear transformation into prompt . We can obtain the input of the empathy decoder Z = [ X ; prompt; Y ] , where X and Y represent the context and target response, respectively. We use the standard maximum likelihood estimate to optimize the response prediction, and we obtain another loss function through the decoder: L res =  log (cid:0) p ( Y | R generate ) (cid:1) . Finally, all the parameters are jointly trained end-to-end to optimize the listener selection and response generation by minimizing the sum of two losses: L = L + L . (6) 4 Experiments 4.1 Dataset Data Pre-Processing The MPED data is obtained from an online peer-to-peer support platform, where users can express their emotions by chatting with others who have similar experiences. Generally, we permit the words of each utterance to range between 3 and 100, excluding emojis, which are stored separately 1 . We discard artificially repeated characters, correct spelling errors, and standardize network language. Developing a dialogue model requires more ethical considerations. Therefore, we focus our analysis on help-seeking or emotional comfort-seeking conversations. As a result, the conversations with sensitive contents are filtered out. In the end, we further ensure that no private information is included. 1 Emotional utterances have been incorporated in MPED yet not in our proposed baseline since we focus on unimodal text in this study. It is quite beneficial that emotional category labels are available, which saves a lot of manual work. We have confirmed their accuracy and constructed the MPED dataset with kinds of emotions. We further classify these emotions for simplicity into 10 types, that is, happy , sad , calm , angry , excited , exhausted , supportive , bored , nervous , and thankful . MPED includes single-turn and multi-turn dialogue data, called MPED-S and MPED-M. We randomly split them into 80% training set, 10% validation set, and 10% testing set, respectively. Empathetic Pre-Processing Given that empathy is a complex feeling, gathering empathetic data is challenging. We first remove the conversations that do not contain empathetic posts, such as games, and so forth. Then, we design a three-point scale (0 to 2) and evaluate empathy, where three criteria are used: Emotional Reactions (expressing warmth and compassion), Interpretation (articulating understanding of feelings and experiences), and Exploration (exploring feelings and experiences not stated in the post). Considering manually screening dialogues is infeasible on large-size data, we filter out simple replies and label single-turn dialogues. In the end, three degrees of empathy are included in MPED, that is, weak , moderate , and strong . The hyper-parameters in our approach are set as follows. The input embeddings are 300-dimensional pre-trained 840B GloVe vectors. The speaking coefficient c is 5. The learning rate is 0 . 003 and batch size is 16 . The dropout rate is 0 . 6 , while the loss weight is 5 e  4 . Automatic Evaluation Criteria We calculate the AVG BLEU (average of BLEU-1,-2,-3,-4) (Pa-pineni et al., 2002) and ROUGE-L (Lin, 2004) scores as evaluations of model response generation, which have been often used to compare the system-generated response against the human-gold response in generation tasks. Human Evaluation Criteria We randomly collect 100 dialogue samples and their corresponding generations from each model. Then, we assign human annotators to rate each response between 1 and 5 on three distinct attributes:  Empathy : assesses whether the speaker of the response understands the feelings of others and fully manifests it;  Relevance : evaluates whether the generated response is relevant with the dialogue context and consistent with the expressed information or background knowledge;  Fluency : measures whether the response is smooth and grammatically correct. MReCoSa: A context-sensitive model with multihead self-attention (Zhang et al., 2019). Multi-Trans: This multi-task model learns emotion classification and dialogue generation at the same time (Rashkin et al., 2018). MoEL: This model (Lin et al., 2019) combines the response representations from multiple emotion-specific decoders. EmpGD: This method (Li et al., 2020b) exploits coarse-grained and fine-grained emotions by an adversarial learning framework. Caire: This method (Lin et al., 2020) fine-tunes a large-scale pre-trained language model with multiple objectives: response language modeling, response prediction, and dialogue emotion detection. Random Prompt: We built a network with random values for prompt according to Lester et al. (2021). We describe the variants of our model below: Graph-Based: This simple model uses a graph-based model to build the empathetic dialogue graph of multi-party. Two-Step Graph: This model adopts a graph network with two-step graph update. SDMPED without Sensibility (SDMPED w/o S): This model ignores the sensibilities of speakers but maintains a TDGCN structure. SDMPED: Our final model combines dynamic emotions with static sensibilities to produce empathy responses. Automatic Evaluation Results According to the experimental results shown in Table 1, our model SDMPED achieves the highest scores under most metrics compared with other baselines. The noticeable improvement indicates the effectiveness of SDMPED on empathetic expressions of multi-party. Since multi-party dialogues are not time-sequential 303 Model MPED-M MPED-S Metrics ROUGE-L AVG BLEU Emp. and multi-turn dialogues need to consider the im-pact of each turn, SDMPED performs better than the models MoEL, EmpDG, and Caire that are designed solely for two-party dialogue. Compared with the Random prompt model, our model has been greatly improved, which demonstrates that our emotional prompt design plays an important role. Given that persons have different sensibilities, adding the characteristics of different people to explore their conversations helps improve the performance. Thus, SDMPED obtains a performance improvement on the basis of SDMPED without Sensibility. Human Evaluation Results Table 1 shows that SDMPED has achieved good performance in Empathy , Relevance , and Fluency . Our model is effective in capturing different emotional changes between multiple speakers and generating appropriate responses. MoEL and EmpDG are more inclined towards the characteristics of two-party dialogues, and thus cannot fully adapt to the new situation of multi-party. Random prompt and Caire are basically as good as our model in Fluency , however their Empathy and Relevance are inferior. These two models are pre-trained transfer learning models, and the generated responses are fluent and grammatical while being simple and general. We perform an ablation study to better understand the contributions of the main parts of our model. As shown in Table 2, the performance becomes notice-0 5 10 15 20 25 30 10 11 12 13 14 15 16 17 18 3 4 5 6 7 8 8+ N u m b er o f W o r d s M e t r i c s Number of Speakers Avg. ably worse, especially in the multi-turn dialogue data, after we remove the sensibility component. The degree of empathy for empathetic dialogues depends on the emotional tone at that time and the speakers' own abilities of perspective-taking, so studying sensibilities can help better investigate the responses generated by different people. According to the comparison of SDMPED without Sensibility and Two-Step Graph, emotions of people change at every moment, and updating the graph structure at each emotional turn is particularly necessary. After removing the two-step graph update mechanism, we find that the results of Graph-Based have further declined, which indicates that the two-step graph convolution process can better extract empathetic and dialogue features. We investigate the effects of different numbers of speakers and tokens. When 37 speakers are available, as shown in Figure 4, the model maintains fairly stable results, indicating that it can handle multiple-party empathetic dialogues effectively. However, the results decline as the speaker number continues to increase. The reason for the drop is 304 Speaker Sensibility Utterance Context Speaker 1 I am alone and have no friends now . that our conversations are typically concentrated between 3 to 5 people, and those with more than 7 people contain little content per speaker. In Figure 5, we compare our model with two prompt embedding methods and different numbers of emotion classification categories. The comparison between the orange and blue curves shows that dividing emotions into 10 categories gives better results than the 6 and 60 categories (6 and 60 categories are similar to the number of categories in MELD and ED datasets). Clearly, dividing emotions into 10 categories and placing a prompt matrix with 2 tokens before the response can yield promising performance. We apply different speakers' sensibilities to the empathy decoder in the same multi-turn conversation context and obtain results based on MPED in Table 3. When presented with Speaker 1 's loneliness and depression, the following four speakers are willing to provide support, but they come up with different responses due to their different sensibilities. Speaker 2 is relatively unable to appreciate the emotions of Speaker 1 and jokes that he/she can find a virtual friend to hug; Speaker 3 expresses warmth and Speaker 4 and Speaker 5 comfort Speaker 1 and express their understanding. They also look forward to the future by suggesting that Speaker 1 can do something that helps distract himself/herself. We have introduced a novel task called MultiParty Empathetic Dialogue Generation.We have proposed a model called SDMPED suitable for the characteristics of the task. Our experiments have demonstrated that SDMPED is superior to other approaches on MPED. Future work can explore related issues such as integrating empathy into the dialogues, combining emojis and responses, guiding the active development of conversation. Data Collection. We collected publicly available data and removed all personal information (phone, email, postcode, location, and any other privacy information). Any potentially sensitive dialogues were completely removed from our data. No treatment recommendations or diagnostic claims were given in this study. This research is approved and monitored by the University's Institutional Review Board and performed in accordance with the principle of GDPR (General Data Protection Regulation 2 ) as follows: data processing shall be lawful if it is necessary for the performance of a task carried out in the public interest. Additionally, this study is explored not for any commercial use while merely for scientific 2 https://gdpr-info.eu/. Annotator Compensation. We resorted to the Amazon Mechanical Turk crowdsourcing platform to evaluate three artificial indicators (i.e., Empathy, Relevance, and Fluency). The crowdworkers were assessed with 20 random sentences, which averagely took 5-6 minutes to accomplish, and compensated with $0.8 per HIT (Human Intelligence Task). The compensation was determined based on the US minimum wage of $7.12 per hour. Potential Misuse. Our model is less likely to contribute to depression of users or generate non-empathic expressions (e.g., discrimination, criticism, and antagonism), since the model is based on the assumption that everyone has varying degrees of sensibility and empathy. Additionally, this model removes any sensitive information of users, and it is basically impossible to infer their personalities, preferences, interests, or other private information from the generated dialogues. Acknowledgements : This work was supported in part by the National Natural Science Foundation of China (No. 62106091) and Shandong Provincial Natural Science Foundation (No. ZR2021MF054).
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