A Cognitive Stimulation Dialogue System with Multi-source Knowledge Fusion for Elders with Cognitive Impairment
Year: 2023

Abstract:
When communicating with elders with cognitive impairment, cognitive stimulation (CS) help to maintain the cognitive health of elders. Data sparsity is the main challenge in building CS-based dialogue systems, particularly in the Chinese language. To fill this gap, we construct a Chinese CS conversation (CSConv) dataset, which contains about 2.6K groups of dialogues with therapy principles and emotional support strategy labels. Making chit chat while providing emotional support is overlooked by the majority of existing cognitive dialogue systems. In this paper, we propose a multi-source knowledge fusion method for CS dialogue (CSD), to generate open-ended responses guided by the therapy principle and emotional support strategy. We first use a progressive mask method based on external knowledge to learn encoders as effective classifiers, which is the prerequisite to predict the therapy principle and emotional support strategy of the target response. Then a decoder interacts with the perceived therapy principle and emotional support strategy to generate responses. Extensive experiments conducted on the CSConv dataset demonstrate the effectiveness of the proposed method, while there is still a large space for improvement compared to human performance1.

heading: 1 Introduction
text: Dialogue systems have enjoyed rapid progress in recent years, through communication with humans to satisfy diverse needs (Liu et al., 2021; Kann et al., 2022). Cognition stimulation of elders is a critical psychological therapy where dialogue systems serve as effective tools for restoring the cognition of older adults (De Oliveira et al., 2014; Park et al., 2019; Tokunaga et al., 2021).
Some studies have shown that chit-chat can help older people with cognitive restoration (van Rijn
1Our data and code could be found in https://github. com/jiangjyjy/CSD
3

heading: I cannot decide. I guess teacher is better. (None, Expression , Selfdisclosure)
text: et al., 2010; Garcia, 2022). Meanwhile, several studies have shown that emotional support is beneficial for maintaining or even increasing cognitive function in elders (Ellwardt et al., 2013; Liu et al., 2020; Sharma et al., 2020). Nonetheless, there remains an open question on how to introduce emotional support and therapy principles simultaneously into chit-chat dialogue systems to provide cognitive recovery training for elders with cognitive impairment.
One main obstacle to building cognitive dialogue is the lack of training corpora, especially in the Chinese language. Therefore, we first construct a Chinese CS Conversation (CSConv) dataset, containing about 2.6K groups of dialogue data where each utterance is annotated with three types of labels, i.e., therapy principle, emotional labels, and emotional support strategy labels. To generate openended responses with emotional support strategies,
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we propose a multi-source knowledge fusion in a Chinese CS Dialogue (CSD) system. We use Jiagu2, a Chinese NLP toolkit, to extract emotional words and keywords to form knowledge source and progressively mask the extracted knowledge on the encoder side, to increase the generalizability of the model. Meanwhile, we adopt Chinese EmoBank (Lee et al., 2022) to calculate the weight value of each word in the utterance, so that the model pays more attention to words with high values. By introducing multiple sources of external knowledge, we greatly enrich the content of the conversation. Moreover, we judge the content and emotions that elders express which is critical to generate satisfied responses, matching them with the cognitive therapeutic principles, and coming up with corresponding supporting strategies. At last, we design a multi-source interactive mechanism so that emotional support strategies and cognitive stimulation therapies can be reasonably combined to generate responses benefiting to mental health. Figure 1 shows an example of a conversation with an elder based on the therapy principle.
In summary, our contributions are as follows: (1) We construct a Chinese CS-based conversation dataset to facilitate the following research; (2) We propose a progressive mask method for encoder modules, which enhances the generalizability on emotional knowledge and the applicability of the therapeutic conversations with elders; (3) We design a multi-source interactive method to model the interaction among encoder modules, decoder modules and external knowledge; (4) We conduct extensive experiments to demonstrate the effectiveness of the proposed CSD.

heading: 2 Dataset

heading: 2.1 Data Collection
text: There is no publicly available CS-based Chinese conversation dataset to enable a cognitively restorative dialogue system for elders with cognitive impairment. We introduce a Chinese one-to-one open-domain CS Conversation dataset, (CSConv), which is collected and created via cognitive stimulation therapy videos and handbook3, and the ratio of conversation data from videos to those from the handbook is approximately 2:1.
As high-quality conversation examples are needed for building Chinese CS-based dialogue
2https://github.com/ownthink/Jiagu 3https://www.brainlive.socialwork.hku.hk/
system, our efforts include the following. (1) The videos are Cantonese. We first translate the Cantonese conversations in the videos into Mandarin Chinese, in a format suitable for CS model training. (2) We make Mandarin conversations artificially based on the eighteen therapy principles in the handbook. (3) We clean the dataset based on rules (e.g., truncating excessively long utterances, removing the multiple consecutive symbols in the utterance). (4) We manually annotate whether each utterance is spoken by the SPEAKER or the LISTENER (SPEAKER for elder, LISTENER for smart speaker or health care worker). (5) We use BERT-based text classification models to annotate the emotion label, strategy label, therapy label of each utterance, and then conduct manual review and modification. (6) All the data are professionally produced and reviewed twice. (7) We test our CSConv dataset on some text classification models and text generation models, which can directly reflect the performance differences between models.
The CSConv dataset consists of about three thousand conversations, separated by blank rows. Each line in each conversation represents the utterance of SPEAKER or LISTENER, and SPEAKER and LISTENER’s utterances alternate. The format of each line is: SPEAKER/LISTENER utterance + <CS> + therapy label + <EMO> + emotion label + <strategy> + strategy label, where <CS> is the separator of therapy label and SPEAKER/LISTENER utterance; <EMO> is the separator of therapy label and emotion label; <Strategy> is the separator of emotion label and strategy label. There are eight types of emotional labels, namely none, disgust, sadness, fear, surprise, like, happiness and anger. There are nine strategies (i.e., None, Question, Reflection of Feelings, Self-disclosure, Providing Sug-
gestions, Information, Others), which are similar to the strategies in (Liu et al., 2021). There are seven types of therapy labels. Table 1 shows the name of explanation of each therapy label.

heading: 2.2 Data Statistics
text: Statistics of the CSConv dataset are given in Table 2. The number and proportion of therapy labels, emotion labels and strategy labels are shown in Table 3.

heading: 3 Method

heading: 3.1 Overview
text: Figure 2 gives an overview of our Chinese CSD architecture, which consists of two stages: (1) Progressive mask encoder; (2) Multi-source interactive
decoder. The first stage is divided into two modules: progressive mask encoder for context training and encoders for text classification.

heading: 3.2 Progressive Mask Encoder
text: Progressive Mask Encoder for Context Training. Like the traditional BERT pre-training task, in order to better represent information of the utterances and evaluate the Next Sentence Prediction (NSP) task, the utterances of the SPEAKER and LISTENER are used to generate three types of embeddings (Vaswani et al., 2017), namely word embedding, position embedding and segment embedding.
During training, the encoder randomly masks tokens to improve generalizability. We first use Jiagu’s sentiment analysis function to extract entities (i.e., one and multiple words) and sentences with positive or negative values generated by Jiagu greater than the λemo threshold, and Jiagu’s keyword extraction function to extract keywords in the utterances. Eventually, emotion and keyword dictionaries are constructed. Through the emotion and keyword dictionaries, the data during training is masked in pre-defined proportions. As the training progresses, the span of a single mask gradually increases (i.e., from one word to multiple words, and finally to a sentence), the ratios of masking one-word entities, two-word entities, three-word entities, four-word entities and sentences are λ1, λ2, λ3, λ4 and λ5, respectively. In order to further improve the encoder’s generalization through the progressive mask method, we retain a certain proportion of the traditional BERT mask method. To be more specific, 5% of the entities in the utterances are randomly masked, of which 80% proceed mask processing, 10% proceed random replacement processing, and 10% remain unchanged.
After the progressive mask operation, encoders are used to encode context information for the utterances (i.e., context learning) and finally the pretrained models are obtained.
Encoders of context training based on the emotion dictionary are used for utterance emotion classification. Encoders based on the keyword dictionary are used to classify the therapy principle and support strategy of the utterances.
Encoders for Text Classification. A multiturn dialogue context consists of M utterances emitted by SPEAKER and LISTENER in turn. The context U refers to the sequence of utterance,
4
i.e., U = [U1, ..., UM ]. Following (Lin et al., 2019), we flat U into a token sequence and insert a CLS token at the start of the token sentence, i.e., U = [CLS, x1, ..., xm].
hi = LN(x l−1 i +MHAtt(x l−1 i )) (1)
x̃li = LN(hi + FFN(hi)) (2)
where LN is the layer normalization proposed by (Ba et al., 2016). MHAtt is multi-head attention, which runs through an attention mechanism several times in parallel (Vaswani et al., 2017). FFN is a two-layer feed-forward network with ReLU as the hidden activation function. The encoder contains l layers. hi is the hidden state of the i-th token and x̃li is the embedding with context of the i-th token at the l layer. The obtained context representations are denoted as Cu = [x̃0, ..., x̃m]. Let lcs be the label of the therapy classification result, i.e.,
lcs = CNN(Cu) (3)
where CNN is a TextCNN classifier (Kim, 2014) with convolution kernel sizes (2,3,4) and 256 convolution kernels. Similarly, lemo and lstr are obtained, representing the labels of the emotion classification result and the strategy classification result, respectively.

heading: 3.3 Multi-Source Interactive Decoder
text: In the decoder generation module, we further insert a SEP token at the end of every utterance in order to distinguish the utterances between SPEAKER and LISTENER in multiple rounds of conversation, i.e., U = [CLS, x1, ..., xm, SEP].
In order to generate responses more suitable for our scenario, encoders, external knowledge and decoder interact in three aspects: (1) input layer; (2) cross-attention mechanism; (3) attention loss.
Input Layer. We take the therapy label lcs, emotional label lemo, and strategy label lstr that encoder classification models generate as three tokens (temo, tcs, tstr) and append them at the end of each utterance. We can then obtain decoder input tokens Y = [y1, ..., yj , temo, tcs, tstr]. To represent sentences and knowledge, we first use a word embedding layer, a positional embedding layer to convert each token into vectors (Vaswani et al., 2017), i.e., EW (yj) ∈ Rd, EP (yj) ∈ Rd, where d is the dimensionality of embeddings. yj is computed as follows: [y1, ..., yj , temo, tcs, tstr] is the composition of two types of embeddings.
Cross-Attention Mechanism. We first train an extra encoder that flattens the input data (the format of the data is the same as that of the decoder input), and get the corresponding hidden states hej:
hej = LN(y l−1 j +MHAtt(y l−1 j )) (4)
In order to more reasonably embed the representation of SPEAKER/LISTENR’s utterances generated by encoders into the decoder through crossattention mechanism, we extract the hidden states from the encoder classification models to replace the hidden states of the labels position (heemo, hecs, hestr) generated by extra encoder, forming new encoder hidden states embedded in the cross attention of decoder.
Attention Loss. Since humans naturally pay extra attention to emotional support and therapy information during a conversation, we enforce an emotional attention loss and keyword attention loss in order to focus on those words with higher emotion intensity values and keyword intensity values. Emotional intensity values and keyword intensity values are obtained from Chinese Emobank and Jiagu, respectively.
To highlight emotional information, we compute emotion intensity values for dialogue words and
external concepts yj :
ηemo(yj) = (Va(yj) +Ar(yj))− 2 ∗ Rmin
Rmax − Rmin (5)
where Va(yj) and Ar(yj) denote the mean values of valence and arousal dimensions of word yj , respectively. Rmin and Rmax represent the minimal and maximal values of the value range, respectively. If yj is not in Chinese EmoBank, ηemo(yj) will be set to 0.
To highlight keyword information, keyword intensity values for dialogue words yj are used based on Jiagu’s keyword extraction function:
ηkw(yj) = softmax(yj) (6)
where the softmax operation calculates a probability for every word and the probabilities of all the words add up to one.
Emotion loss Lemo and keywords loss Lkw are calculated using Mean Square Error (MSE).
Lemo = 1
e ×
e∑
i=1
(ηemo(yj)− aj)2 (7)
Lkw = 1
e ×
e∑
i=1
(ηkw(yj)− aj)2 (8)
where aj is the attention weight of each word in the utterance calculated by the attention output tensors.
When the model generates the response, we use a sampling method to generate the next j-th token Given U and tokens temo, tcs and tstr, our multi-source interactive decoder aims to generate a n-length response Y = {y1, ..., yn} through maximizing the probability P(Y|U , temo, tcs, tstr) =∏N
n=1 P(yn|y<n,U , temo, tcs, tstr). Like most dialogue generation tasks, standard maximum likelihood estimator (MLE) is used as the optimization objective:
Lgen = −log(P(Y|U , temo, tcs, tstr)) (9)
Eventually, a joint loss function is defined to jointly minimize the emotion attention loss (Eq. 7), the keywords attention loss (Eq. 8) and the generation loss (Eq. 9) as follows:
L = γ1 ∗ Lgen + γ2 ∗ Lemo + γ3 ∗ Lkw (10)
where γ1, γ2 and γ3 are hyper-parameters.

heading: 3.4 Training
text: We divide training into three phases as follows: (1) Encoders are used for context training based on the progressive mask method. Two pre-trained encoder models are trained based on sentiment dictionary and keyword dictionary, respectively. (2) Therapy classification and strategy classification tasks are realized on the basis of the encoder trained according to the keyword dictionary. The task of emotion classification is realized based on the encoder trained according to the emotion dictionary. (3) We use the flatten data as the training data of the encoder, making the batch size and input data consistent with the decoder. Then the hidden state of the last layer of the encoder is interacted with the decoder through the cross attention mechanism.

heading: 4 Experiments

heading: 4.1 Implementation Details
text: We conduct experiments on the CSConv dataset. For the encoder module of the CSD, the pre-trained model is bert-base-chinese4, and the decoder module is gpt2-chinese-cluecorpussmall (Du, 2019). Most of the hyperparameters are the same as those in decoder chitchat5. In the progressive mask encoder trained based on the keyword dictionary, the ratios of masked entities and sentences (i.e., λ1, λ2, λ3, λ4 and λ5) are set as 0.9, 0.9, 0.9, 0.9 and 0.4, respectively. Based on the emotion dictionary, λ1, λ2, λ3, λ4 and λ5 are set as 0.5, 0.5, 0.4, 0.3 and 0.2, respectively. Loss weights, namely γ1, γ2 and γ3, are set as 1, 0.5 and 0.5, respectively. We implement all models with PyTorch (Paszke et al., 2019) on four NVIDIA A100 GPUs, and train the models using AdamW optimizer (Loshchilov and Hutter, 2017) with a batch size of 4. We vary the learning rate during training following (Vaswani et al., 2017). For inference, we set the temperature as 0.7, top-k as 8 and top-p as 0.5. The training time for the encoder of the CSD is about 2 minutes and that for the decoder is about 33 minutes. In testing different models, we use NLTK packages to compute the Bleu metric and bert-score package to compute BERTScore. We set the smooth function of NLTK to method 7, and the model used in computing the bert-score is bert-base-chinese.
4https://huggingface.co/bert-base-chinese 5https://github.com/yangjianxin1/
GPT2-chitchat

heading: 4.2 Automatic Evaluation
text: For encoder classification, to evaluate the model at the emotional level, we adopt Emotion Accuracy as the evaluation metric between the ground truth emotion labels and the predicted emotion labels. Therapy Accuracy and Strategy Accuracy are similar evaluation metrics to emotion accuracy.
For decoder generation, we employ BLEU (Papineni et al., 2002), an algorithm for evaluating the text quality, as the metric. Since BLEU cannot perfectly reflect the quality of generated results (Liu et al., 2016), we adopt BERTScore (Zhang et al., 2019a) to compare the similarity between embeddings of a generated sentence and the reference sentence. Distinct-1 / Distinct-2 (Li et al., 2016) is the proportion of the distinct uni / bi-grams in all the generated results, that indicate the diversity.

heading: 4.3 Human Evaluation
text: To qualitatively examine model performance, we also conduct human evaluations. We sample some dialogues from the CSD and the baselines. We find 6 elders and their relatives to evaluate the responses generated by different models. All models are evaluated in terms of Empathy, Support and Fluency. Empathy measures whether LISTENER understands SPEAKER’s feelings. Support measures whether LISTENER gives SPEAKER reasonable help and comfort. Fluency measures the grammatical correctness and readability of the SPEAKER’s responses. Each metric is rated on five-scale, where 1, 3 and 5 indicate unacceptable, moderate and excellent performance, respectively.

heading: 4.4 Baselines for Comparison
text: We conduct extensive experiments to compare the encoder module of the CSD against the following representative baselines: (1) Transformer (Vaswani et al., 2017): A transformer-based encoder-decoder model. (2) BERT (Kenton and Toutanova, 2019): BERT is a context-aware encoder, and is good at processing downstream tasks, like classification. (3) BERT+CNN6: The model is the embedding with contextual meaning output by BERT, which is input into a CNN classifier for classification.
We conduct extensive experiments to compare the decoder generation module of CSD against the following representative baselines: (1) CDialGPTbase (Wang et al., 2020a): The model is a 12-layer GPT model trained on the LCCC-base dataset. (2) CDialGPT-large (Wang et al., 2020a): The model is a 12-layer GPT model trained on the LCCClarge dataset. (3) GPT2-chitchat7: The model is a 10-layer GPT-2 trained on 500,000 chitchat corpus. (4) Distil-cluecorpussmall (Radford et al., 2019): The model is a 6-layer GPT-2 trained on the CLUECorpusSmall (Xu et al., 2020; Du, 2019) corpus. (5) Cluecorpussmall (Radford et al., 2019; Du, 2019): The model is a 12-layer GPT-2 trained on the CLUECorpusSmall corpus.
To better analyze the influence of different components in the CSD, we also conduct an ablation
6https://github.com/649453932/ Bert-Chinese-Text-Classification-Pytorch
7https://github.com/yangjianxin1/ GPT2-chitchat
study as follows: (1) w/o NM: The CSD model uses only traditional BERT instead of BERT trained using the progressive mask method. (2) w/o IL: The CSD model only splices three classification result labels after utterance as the train data. (3) w/o CA: The CSD model only interacts with encoder through the cross-attention mechanism. (4) w/o AL: The CSD model only adds the attention loss to embed external knowledge.

heading: 4.5 Experimental Results and Analysis
text: Automatic evaluations. In Table 4, we observe that the encoder module of the CSD is better than the other baselines in therapy, emotion, support strategy recognition accuracy. In Table 5, we observe that the CSD outperforms strong baselines in terms of Bleu and BERTScore. Because CSD models extensive therapy principle and emotional support strategy and there is less language diversity associated with therapy principle and emotional support strategy, the diversity of response is weaker than that of CDialGPTbase and CDialGPTlarge.
We also perform an ablation study for better understanding the contributions of the main modules of the CSD model. As shown in Table 6, CSD outperforms all other models (w/o NM, w/o IL, w/o CA, w/o AL) in Bleu and BERTScore. However, due to therapy principle and emotional support strategy intervening in the generation of decoders, the diversity of response generation decreases. Only the case of w/o CA model involving a small number of therapies and support strategies achieves high diversity of generated responses.
Human evaluation. Table 5 illustrates that CSD obtains the best performance on Empathy, Support
and Fluency scores. Additionally, we carry out pairwise response comparison to directly compare the dialogue quality gains in Table 7. The results confirm that the responses from CSD are more preferred by human judges.

heading: 4.6 External Knowledge Analysis
text: We introduce external knowledge in three ways: training encoders by using external knowledge to progressively mask entities and sentences (w/o NM), intervening GPT-2 generation by classification labels (w/o IL), and paying more attention to emotional words and keywords by calculating the weight of words (w/o AL). To further investigate the impact of introduced knowledge, we test different components of CSD as shown in Table 6. However, the distinct metrics of these models are lower than models without embedded knowledge (w/o CA). Because w/o NM has more knowledge embedded than w/o IL and w/o AL and distinct metric of w/o NM is also significantly improved compared with w/o IL and w/o AL, it concluded that the generated response diversity decreases when little external knowledge is embedded, but with the increase of embedded knowledge, diversity of the generated response also increases.

heading: 4.7 Case Study
text: For decoder generation evaluation, Table 8 shows two examples generated by CSD and other baselines. In the first case, CSD generates an informative response with proper therapy principle and emotional support, which stimulates thinking of the elder through implicit empathy and further questioning. However, baselines with only the decoder part fail to express responses with the therapy principle and emotional support. In the second case, CSD generates a response with continuous questions, which further stimulates thinking of elder. Both cases show that CSD can generate responses with therapy principle and emotional support.

heading: 5 Related Work

heading: 5.1 Cognitive Training Dialogue System
text: With the increasing popularity of NLP, dialogue systems have progressed from exploiting simple neural networks (Lee et al., 2016) to large-scale pretrained models (Vlasov et al., 2019; Zhang et al., 2019b; Ni et al., 2022). Currently, while English dialogue systems dominate, there also exist Chi-
nese ones8 (Wang et al., 2020b; Zhou et al., 2021; Gu et al., 2022). Most of these dialogue systems are for ordinary people, and there are few cognitive recovery dialogue systems for elders with cognitive impairment. Most of the existing dialogue systems for elders focus on specific functions, such as storytelling (Tokunaga et al., 2019, 2021), robotic dialogue based on photos (Tokunaga et al., 2021), etc. There are also dialogue systems for Metamemory therapy (Kim et al., 2021b). Few dialogue systems exist on cognitive stimulation (Navarro et al., 2018), let alone in Chinese.

heading: 5.2 Empathetic Dialogue, Emotional Support Conversation and Related Datasets
text: With the rise of data-driven learning methods (Vaswani et al., 2017), there are more and more studies on open-domain dialogue generation patterns (Dinan et al., 2018; Kann et al., 2022). In order to generate an emotional response, many methods automatically recognize the current user’s emotional state through the conversation (Sabour et al., 2022; Gao et al., 2021; Kim et al., 2021a; Shen et al., 2021; Welivita and Pu, 2020; Lin et al., 2019). (Li et al., 2020) propose a multiresolution adversarial framework which considers multi-granularity emotion factors and user feedback. (Li et al., 2022) propose a knowledge-aware empathetic dialogue generation method, which interferes with generation by embedding external
8https://github.com/yangjianxin1/ GPT2-chitchat
knowledge into the Transformer model via diagrams. Some studies (Sharma et al., 2020, 2021) on empathetic dialogue technologies have also been applied to mental health. About dataset, EMPATHETICDIALOGUES (Rashkin et al., 2019) is the benchmark of the empathetic dialogue datasets, but there exist very few relevant datasets in Chinese.
Different from empathetic dialogue, emotional support conversation can provide emotional support and problem solving in addition to empathetic responses (Liu et al., 2021). Because the field is new, there are a few studies on emotional support conversation (Tu et al., 2022; Peng et al., 2022; Xu et al., 2022). (Tu et al., 2022) propose MISC, which is a mixed strategy-aware model integrating COMET for emotional support conversation. ESConv (Liu et al., 2021) is the benchmark of the emotional support conversation datasets, but there is no Chinese emotional support conversation dataset.

heading: 6 Conclusion and Outlook
text: In this paper, we construct a Chinese CS conversation dataset and propose a multi-source knowledge fusion method for CS dialogue. Experimental results show that CSD outperforms state-of-the-art models in terms of both automatic and human evaluations. Extensive experiments verify the effectiveness of the progressive mask method and the three interaction ways of multi-source interactive decoder in CSD. As for future work, we plan to construct larger datasets of Mandarin and Cantonese
CS conversations to train models, and address the issue of therapy principle, emotional support recognition in reference context in dialogue.
Limitations
The current dialogue system is mainly based on deep neural network, like transformer structure, which often requires a large number of data sets for training model. However, there are still some deficiencies in our dataset. We will further label and create more dataset to train model. In addition, in order to improve the quality of dialogue, our model parameters are relatively large, which affect the speed of dialogue generation to some extent. We will explore some methods, such as knowledge distillation, to reduce model parameters to improve the speed of dialogue generation on the premise of keeping the quality of dialogue generation unchanged.
Ethics Statement
We have sought to ethically conduct this study, including transparently communicating with data annotators about data use and study intent, and finding suitable elders to conduct human tests of the dialogue systems, compensating workers and elders at a reasonable hourly wage. We have obtained study approval from the ethics review board.

heading: Acknowledgements
text: We want to thank our anonymous AC and reviewers for their feedback. This work was supported in part by grants from Hong Kong Research Grants Council (RGC) under the contracts HKU 17203522 and 17207621.

heading: A For every submission:

heading: 3 A1. Did you describe the limitations of your work?
text: Limitation part.
7 A2. Did you discuss any potential risks of your work? There is no potential risk in our work.

heading: 3 A3. Do the abstract and introduction summarize the paper’s main claims?
text: Abstract part and part 6.
7 A4. Have you used AI writing assistants when working on this paper? Left blank.
B 7 Did you use or create scientific artifacts? Left blank.
B1. Did you cite the creators of artifacts you used? No response.
B2. Did you discuss the license or terms for use and / or distribution of any artifacts? No response.
B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? No response.
B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? No response.
B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? No response.
B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. No response.
C 3 Did you run computational experiments? Section 5.1 of Part 5.

heading: 3 C1. Did you report the number of parameters in the models used, the total computational budget
text: (e.g., GPU hours), and computing infrastructure used? Section 5.1 of Part 5.
The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance.

heading: 3 C2. Did you discuss the experimental setup, including hyperparameter search and best-found
text: hyperparameter values? Section 5.1 of Part 5.

heading: 3 C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary
text: statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 5.1 of Part 5.

heading: 3 C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did
text: you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 5.1 of Part 5.
D 3 Did you use human annotators (e.g., crowdworkers) or research with human participants? Ethnics statement part.

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.? Ethnics statement part.

heading: 3 D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students)
text: and paid participants, and discuss if such payment is adequate given the participants’ demographic (e.g., country of residence)? Ethnics statement part.

heading: 3 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
text: crowdworkers explain how the data would be used? Ethnics statement part.

heading: 3 D4. Was the data collection protocol approved (or determined exempt) by an ethics review board?
text: Ethnics statement part.
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: As for future work, we plan to construct larger datasets of Mandarin and Cantonese
CS conversations to train models, and address the issue of therapy principle, emotional support recognition in reference context in dialogue. Limitations
The current dialogue system is mainly based on deep neural network, like transformer structure, which often requires a large number of data sets for training model. However, there are still some deficiencies in our dataset. We will further label and create more dataset to train model. In addition, in order to improve the quality of dialogue, our model parameters are relatively large, which affect the speed of dialogue generation to some extent. We will explore some methods, such as knowledge distillation, to reduce model parameters to improve the speed of dialogue generation on the premise of keeping the quality of dialogue generation unchanged