Original: EmpHi: Generating Empathetic Responses with Human-like Intents
Revised: EmpHi: Generating Empathetic Responses with Human-like Intents

Original: Anonymous ACL submission

Original: Abstract
Revised: Abstract

Original: In empathetic conversations, humans expresstheir empathy to others with empathetic intents.
Revised: In empathetic conversations, humans express their empathy to others with empathetic intents.

Original: However, most existing empathetic conversational methods suffer from a lack of empatheticintents, which leads to monotonous empathy.
Revised: However, most existing empathetic conversational methods suffer from a lack of empathetic intents, which leads to monotonous empathy.

Original: To address the bias of the empathetic intentsdistribution between empathetic dialogue models and humans, we propose a novel modelto generate emp athetic responses with h umanconsistent empathetic i ntents, EmpHi for short.
Revised: To address the bias of the empathetic intents distribution between empathetic dialogue models and humans, we propose a novel model to generate emp athetic responses with h umanconsistent empathetic i ntents, EmpHi for short.

Original: Precisely, EmpHi learns the distribution of potential empathetic intents with a discrete latent variable, then combines both implicit andexplicit intent representation to generate responses with various empathetic intents.
Revised: Precisely, EmpHi learns the distribution of potential empathetic intents with a discrete latent variable, then combines both implicit and explicit intent representation to generate responses with various empathetic intents.

Original: Experiments show that EmpHi outperforms state-ofthe-art models in terms of empathy, relevance,and diversity on both automatic and humanevaluation.
Revised: Experiments show that EmpHi outperforms state-ofthe-art models in terms of empathy, relevance, and diversity on both automatic and human evaluation.

Original: Moreover, the case studies demonstrate the high interpretability and outstandingperformance of our model.
Revised: Moreover, the case studies demonstrate the high interpretability and outstanding performance of our model.

Revised: Our code are avaliable at https://github.com/mattc95/EmpHi.

Original:  Introduction
Revised:  Introduction

Original: Empathy is a basic yet essential human ability inour daily life.
Revised: Empathy is a basic yet essential human ability in our daily life.

Original: It is a capacity to show one’s caring and understanding to others.
Revised: It is a capacity to show one’s caring and understanding to others.

Original: Many types ofresearch have been conducted on empathetic expression to enhance the empathy ability of Artificial Intelligence, e.g., computational approachfor empathy measurement (Sharma et al., 2020),empathetic expression understanding in newswire(Buechel et al., 2018), online mental health support(Sharma et al., 2021), etc.
Revised: Many types of research have been conducted on empathetic expression to enhance the empathy ability of Artificial Intelligence, e.g., computational approach for empathy measurement (Sharma et al., 2020), empathetic expression understanding in newswire (Buechel et al., 2018), online mental health support (Sharma et al., 2021), etc.

Original: In this work, we focus on the task of generating empathetic responses(Rashkin et al., 2019; Lin et al., 2019; Majumderet al., 2020) in open-domain conversation.
Revised: In this work, we focus on the task of generating empathetic responses (Rashkin et al., 2019; Lin et al., 2019; Majumder et al., 2020) in open-domain conversation.

Original: Existing empathetic dialogue models pay moreattention to the emotion-dependent response generation (Lin et al., 2019; Majumder et al., 2020).
Revised: Existing empathetic dialogue models pay more attention to the emotion-dependent response generation (Lin et al., 2019; Majumder et al., 2020).

Original: However, using emotion alone to generate responses is coarse-grained, and the model needsto incorporate empathetic intent information.

Original: On I just started college again, and while I'm doing great in school, it has lead me to feel very lonely with a lack of social life.
Revised: I just started college again, and while I'm doing great in school, it has lead me to feel very lonely with a lack of social life.

Original: I am sorry to hear that!
Revised: I am sorry to hear that!

Original: I know I broke up with my ex, but I can't help but feel irritated when he talks about going on dates.
Revised: I know I broke up with my ex, but I can't help but feel irritated when he talks about going on dates.

Original: A while back my cat knocked over and broke my mother’s urn.
Revised: A while back my cat knocked over and broke my mother’s urn.

Original: Oh my goodness, I have a cat, I know how you feel.
Revised: Oh my goodness, I have a cat, I know how you feel.

Original: I am sorry to hear that .
Revised: I am sorry to hear that .

Original: I hope you find someone to help you.
Revised: I hope you find someone to help you.

Original: That is so annoying.
Revised: That is so annoying.

Original: I would be upset too.
Revised: I would be upset too.

Original: Acknowledging
Revised: Acknowledging

Original: Sympathizing
Revised: Sympathizing

Original: Agreeing
Revised: Agreeing

Original: E m pH
Revised: E m p

Original: i O u t pu t  the one hand, the speaker often talks with a particular emotion while the listener shows their empathy with specific empathetic intents, e.g., Acknowledging , Agreeing , Consoling and Questioning etc (Welivita and Pu, 2020).
Revised: [Figure] pu t However, using emotion alone to generate responses is coarse-grained, and the model needs to incorporate empathetic intent information. On the one hand, the speaker often talks with a particular emotion while the listener shows their empathy with specific empathetic intents, e.g., Acknowledging , Agreeing , Consoling and Questioning etc (Welivita and Pu, 2020).

Original: On the other hand, see in
Revised: On the other hand, see in

Original: Figure 1, when the user expresses sadness, existing models tend to generate sympathetic responses like "I’m sorry to hear that."
Revised: Figure 1, when the user expresses sadness, existing models tend to generate sympathetic responses like "I’m sorry to hear that."

Original: However, empathy is not the same as sympathy, so the models should not only generate responses with Sympathizing intent.
Revised: However, empathy is not the same as sympathy, so the models should not only generate responses with Sympathizing intent.

Original: We demonstrate this phenomenon elaborately with a quantitative evaluation in Section 2.
Revised: We demonstrate this phenomenon elaborately with a quantitative evaluation in Section 2.

Original: In real life situation, humans could reply with various empathetic intents to the same context which depends on personal preference.
Revised: In real life situation, humans could reply with various empathetic intents to the same context which depends on personal preference.

Original: For example, given a context, "I just failed my exam", an individual may respond "Oh no, what happened?" with Questioning intent to explore the experience of the user, or "I understand this feeling, know how you feel" with
Revised: For example, given a context, "I just failed my exam", an individual may respond "Oh no, what happened?" with Questioning intent to explore the experience of the user, or "I understand this feeling, know how you feel" with

Original: Agreeing intent.
Revised: Agreeing intent.

Original: These types of empathy are more relevant, interactive, and diverse.
Revised: These types of empathy are more relevant, interactive, and diverse.

Original: To address the above issues, we propose a new framework to generate empathetic responses withhuman-like empathetic intents (EmpHi) whichcould generate responses with various empatheticintents, see examples in Figure 1.
Revised: To address the above issues, we propose a new framework to generate empathetic responses with human-like empathetic intents (EmpHi) which could generate responses with various empathetic intents, see examples in Figure 1.

Original: Specifically, EmpHi learns the empathetic intent distribution with a discrete latent variable and adopts intent repre sentation learning in the training stage.
Revised: Specifically, EmpHi learns the empathetic intent distribution with a discrete latent variable and adopts intent representation learning in the training stage.

Original: During thegeneration process, EmpHi first predicts a potentialempathetic intent and then combines both implicitand explicit intent representation to generate a response corresponding to the predicted intent.
Revised: During the generation process, EmpHi first predicts a potential empathetic intent and then combines both implicit and explicit intent representation to generate a response corresponding to the predicted intent.

Original: Ourmain contributions are:
Revised: Our main contributions are:

Original: • We discover and quantify the severe bias of empathetic intents between existing empa-
Revised: • We discover and quantify the severe bias of empathetic intents between existing empathetic dialogue models and humans.

Original: thetic dialogue models and humans.

Original: This find- ing will lead subsequent researchers to pay more attention to fine-grained empathetic intents.
Revised: This finding will lead subsequent researchers to pay more attention to fine-grained empathetic intents.

Original: • To address the above problem, we pro- pose EmpHi, which generates responses with human-like empathetic intents.
Revised: • To address the above problem, we propose EmpHi, which generates responses with human-like empathetic intents.

Original: Experiments have proved the effectiveness of our model through the significant improvement in both automatic and human evaluation.
Revised: Experiments have proved the effectiveness of our model through the significant improvement in both automatic and human evaluation.

Original: • According to the quantitative evaluation and

Original:  analysis, EmpHi successfully captures hu- mans’ empathetic intent distribution, and shows high interpretability in generation pro- cess.
Revised: According to the quantitative evaluation and analysis, EmpHi successfully captures humans’ empathetic intent distribution, and shows high interpretability in generation process.

Original:  Related Work
Revised:  Related Work

Original: Empathetic Response Generation.
Revised: Empathetic Response Generation.

Original: Providing dialogue agents the ability to recognize speaker feelings and reply according to the context is challenging and meaningful.
Revised: Providing dialogue agents the ability to recognize speaker feelings and reply according to the context is challenging and meaningful.

Original: Rashkin et al.
Revised: Rashkin et al.

Original: (2019)propose the EmpatheticDialogues for empatheticresponse generation research.
Revised: (2019) propose the EmpatheticDialogues for empathetic response generation research.

Original: Most subsequent empathetic conversation researches are evaluated onthis dataset, including ours.
Revised: Most subsequent empathetic conversation researches are evaluated on this dataset, including ours.

Original: They also propose
Revised: They also propose

Original: Multitask-Transformer, which is jointly trainedwith context emotion classification and responsegeneration.
Revised: Multitask-Transformer, which is jointly trained with context emotion classification and response generation.

Original: To further capture the fine-grained emotion information, Lin et al.
Revised: To further capture the fine-grained emotion information, Lin et al.

Original: (2019) introduce MoEL,a transformer with a multi-decoder.
Revised: (2019) introduce MoEL, a transformer with a multi-decoder.

Original: Each of themis responsible for the response generation of onespecific emotion.
Revised: Each of them is responsible for the response generation of one specific emotion.

Original: Majumder et al.
Revised: Majumder et al.

Original: (2020) propose
Revised: (2020) propose

Original: MIME, which mimics the speaker emotion to avarying degree.
Revised: MIME, which mimics the speaker emotion to a varying degree.

Original: All these models focus on emotion-dependentempathetic response generation, whereas we pay more attention to the empathetic intents and propose to generate a response that is not only emotionally appropriate but also empathetically humanlike.
Revised: All these models focus on emotion-dependent empathetic response generation, whereas we pay more attention to the empathetic intents and propose to generate a response that is not only emotionally appropriate but also empathetically humanlike.

Original: One-to-many Response Generation.
Revised: One-to-many Response Generation.

Original: Given dialogue history, there could be various responses depends on personal preference.
Revised: Given dialogue history, there could be various responses depends on personal preference.

Original: Zhao et al.
Revised: Zhao et al.

Original: (2017) propose to learn the potential responses with continuous latent variable and maximize the loglikelihood using Stochastic Gradient Variational
Revised: (2017) propose to learn the potential responses with continuous latent variable and maximize the loglikelihood using Stochastic Gradient Variational

Original: Bayes (SGVB) (Kingma and Welling, 2014).
Revised: Bayes (SGVB) (Kingma and Welling, 2014).

Original: To further improve the interpretability of response generation, Zhao et al.
Revised: To further improve the interpretability of response generation, Zhao et al.

Original: (2018) propose to capture potential sentence-level representations with discrete latent variables.
Revised: (2018) propose to capture potential sentence-level representations with discrete latent variables.

Original: MIME (Majumder et al., 2020) introduces stochasticity into the emotion mixture for various empathetic responses generation.
Revised: MIME (Majumder et al., 2020) introduces stochasticity into the emotion mixture for various empathetic responses generation.

Original: Different from the previous works, we propose a discrete latent variable to control the empathetic intent of response and achieve intent-level diversity.
Revised: Different from the previous works, we propose a discrete latent variable to control the empathetic intent of response and achieve intent-level diversity.

Original:  Empathetic Expression Bias
Revised:  Empathetic Expression Bias

Original: Although existing empathetic conversational methods have shown promising progress, we reveal there is a severe bias of empathetic expression between them and humans according to quantitative evaluation.
Revised: Although existing empathetic conversational methods have shown promising progress, we reveal there is a severe bias of empathetic expression between them and humans according to quantitative evaluation.

Original: Empathy plays a vital role in human conversation, Welivita and Pu (2020) make a taxonomy of empathetic intents and calculate the frequency of each intent in EmpatheticDialogues (Rashkin et al., 2019).
Revised: Empathy plays a vital role in human conversation, Welivita and Pu (2020) make a taxonomy of empathetic intents and calculate the frequency of each intent in EmpatheticDialogues (Rashkin et al., 2019).

Original: As shown in Figure 2, humans showtheir empathy naturally by Questioning , Acknowledging , and Agreeing intents etc. However, there are no empirical experiments have shown how empathetic dialogue models ex-
Revised: As shown in Figure 2, humans show

Original: I love my friend,she just drove me to class.
Revised: I love my friend,she just drove me to class.

Original: Context Encoder
Revised: Context Encoder

Original: Emotion Classifier
Revised: Emotion Classifier

Original: Intent Keywords
Revised: Intent Keywords

Original: ResponseDecoder
Revised: ResponseDecoder

Original: Wow!
Revised: Wow!

Original: You should do something back for her!
Revised: You should do something back for her!

Original: Vocabulary
Revised: Vocabulary

Original: Prior Network
Revised: Prior Network

Original: Recognition Network
Revised: Recognition Network

Original: Intent Predictor
Revised: Intent Predictor

Original: Context + +
Revised: Context + +

Original: Copy Gate
Revised: Copy Gate

Original: Response 𝜶 1- 𝜶 𝑲𝑳(𝒑|𝒒)
Revised: Response 𝜶 1- 𝜶 𝑲𝑳(𝒑|𝒒)

Original: Emotion
Revised: Emotion

Original: Intent
Revised: Intent

Original: ImplicitGate
Revised: ImplicitGate

Original: Context Attention press their empathy?
Revised: Context Attention their empathy naturally by Questioning , Acknowledging , and Agreeing intents etc. However, there are no empirical experiments have shown how empathetic dialogue models express their empathy?

Original: To further study, we finetunea BERT classifier (Devlin et al., 2019) on the released EmpatheticIntents 1 dataset (Welivita and
Revised: To further study, we finetune a BERT classifier (Devlin et al., 2019) on the released EmpatheticIntents 1 dataset (Welivita and

Original: Pu, 2020).
Revised: Pu, 2020).

Original: Our classifier achieves 87 .
Revised: Our classifier achieves 87 .

Original: 75% accuracy in intent classification and we apply it to identify the empathetic intents of responses generatedby the state-of-the-art empathetic dialogue model
Revised: 75% accuracy in intent classification and we apply it to identify the empathetic intents of responses generated by the state-of-the-art empathetic dialogue model

Original: MIME (Majumder et al., 2020).
Revised: MIME (Majumder et al., 2020).

Original: As shown in Figure 4, the severe empathetic intent distribution biasemerges while comparing humans to MIME.
Revised: As shown in Figure 4, the severe empathetic intent distribution bias emerges while comparing humans to MIME.

Original: Givencontext with sad emotion, existing models usuallygenerate "I am sorry to hear that" with Sympathizing intent, which is not human-like and contextually relevant.
Revised: Given context with sad emotion, existing models usually generate "I am sorry to hear that" with Sympathiz- ing intent, which is not human-like and contextually relevant.

Original: In addition, we can tell that theempathetic expression of MIME is monotonous.
Revised: In addition, we can tell that the empathetic expression of MIME is monotonous.

Original: We also quantify the intent distribution of other empathetic dialogue models in the Appendix.
Revised: We also quantify the intent distribution of other empathetic dialogue models in the Appendix A.

Original: The results are similar to Figure 4.
Revised: The results are similar to Figure 4.

Original: We believe this phenomenon is caused by that existing models only generate responses according to context emotion and lack fine-grained empathetic intent modeling.
Revised: We believe this phenomenon is caused by that existing models only generate responses according to context emotion and lack fine-grained empathetic intent modeling.

Original: Therefore, we propose EmpHi, which generates empathetic responses with humanlike empathetic intents.
Revised: Therefore, we propose EmpHi, which generates empathetic responses with humanlike empathetic intents.

Original:  EmpHi Method 
Revised:  EmpHi Method 4. Task Definition and Overview

Original: 4. Task Definition and Overview

Original: Given the context, C =
Revised: Given the context, C =

Original: [ c 1 , c 2 , · · · , c m ] , which consists of m words for single or multiple utterances.
Revised: [ c 1 , c 2 , · · · , c m ] , which consists of m words for single or multiple utterances.

Original: We aim to generate empathetic response,
Revised: We aim to generate empathetic response,

Original: X =
Revised: X =

Original: [ x 1 , x 2 , · · · , x n ] , with human-like empathetic intent.
Revised: [ x 1 , x 2 , · · · , x n ] , with human-like empathetic intent.

Original: The whole model architecture is shown in Figure 3.
Revised: The whole model architecture is shown in Figure 3.

Original: EmpHi learns the potential empathetic intent distribution with a latent variable z , which could be seen in Figure 5.
Revised: EmpHi learns the potential empathetic intent distribution with a latent variable z , which could be seen in Figure 5.

Original: Conditional Variational AutoEncoder (CVAE)
Revised: Conditional Variational AutoEncoder (CVAE)

Original: (Yan et al., 2016; Zhao et al., 2017;
Revised: (Yan et al., 2016; Zhao et al., 2017;

Original: Gu et al., 2019) is trained to maximize the conditional log likelihood, log p ( X | C ) , which involves an intractable marginalization over z .
Revised: Gu et al., 2019) is trained to maximize the conditional log likelihood, log p ( X | C ) , which involves an intractable marginalization over z .

Original: We train the
Revised: We train the

Original: CVAE efficiently with Stochastic Gradient Variational Bayes (SGVB) (Kingma and Welling, 2014) by maximizing the variational lower bound of the log likelihood:
Revised: CVAE efficiently with Stochastic Gradient Variational Bayes (SGVB) (Kingma and Welling, 2014) by maximizing the variational lower bound of the log likelihood:

Original: [Equation]
Revised: [Equation]

Original: p ( X | C, z ) denotes response reconstruction probability, q ( z | X, C ) is recognition probability and p ( z | C ) is prior probability. Our method mainly consists of three aspects: Z
Revised: [Figure]

Original: X
Revised: X

Original: C C
Revised: C C

Original: X p (𝑧|𝐶) q (𝑧|𝑋,𝐶)p (𝑋|𝐶,𝑧)
Revised: X p (𝑧|𝐶) q (𝑧|𝑋,𝐶)

Original: [Figure]
Revised: [Figure]

Original: [Figure]
Revised: p ( X | C, z ) denotes response reconstruction probability, q ( z | X, C ) is recognition probability and p ( z | C ) is prior probability.

Original: [Figure]
Revised: Our method mainly consists of three aspects:

Original: • To capture the explicit relationship between the latent variable and the intent, we propose an intent representation learning approach to learn the intent embeddings.
Revised: • To capture the explicit relationship between the latent variable and the intent, we propose an intent representation learning approach to learn the intent embeddings.

Original: • We construct an intent predictor to predict potential response intent using contextual in- formation and then use this intent for guiding the response generation.
Revised: • We construct an intent predictor to predict potential response intent using contextual information and then use this intent for guiding the response generation.

Original: • During the generation process, EmpHi com- bines both implicit intent embedding and ex- plicit intent keywords to generate responses corresponding to the given intents.
Revised: • During the generation process, EmpHi combines both implicit intent embedding and explicit intent keywords to generate responses corresponding to the given intents.

Original: 4. Learning Intent Representation
Revised: 4. Learning Intent Representation

Original: To achieve more interpretability, we choose a discrete latent variable that obeys categorical distribution with nine categories, each corresponding toone empathetic intent.
Revised: To achieve more interpretability, we choose a discrete latent variable that obeys categorical distribution with nine categories, each corresponding to one empathetic intent.

Original: Directly maximizing Eq.1would cause two serious problems: the relation between the latent variable and intent is intractable;the vanishing latent problem results in insufficientinformation provided by the latent variable during generation.
Revised: Directly maximizing Eq. would cause two serious problems: the relation between the latent variable and intent is intractable; the vanishing latent problem results in insufficient information provided by the latent variable during generation.

Original: (Bowman et al., 2016;
Revised: (Bowman et al., 2016;

Original: Zhao et al. ,2017; Gu et al., 2019).
Revised: Zhao et al., 2017; Gu et al., 2019).

Original: To solve the above issues, we separately train arecognition network q r ( z | X ) to encourage intentvariable z to capture context-independent semantics, which is essential for z to be interpretable(Zhao et al., 2018).
Revised: To solve the above issues, we separately train a recognition network q r ( z | X ) to encourage intent variable z to capture context-independent semantics, which is essential for z to be interpretable (Zhao et al., 2018).

Original: The task of the recognition network is to provide the accurate intent label of theresponse, which corresponds to an intent embedding.
Revised: The task of the recognition network is to provide the accurate intent label of the response, which corresponds to an intent embedding.

Original: Then, by maximizing likelihood p ( X | C, z ) ,the embedding captures corresponding intent representation automatically.
Revised: Then, by maximizing likelihood p ( X | C, z ) , the embedding captures corresponding intent representation automatically.

Original: The recognition networkq r ( z | X ) does not need additional training.
Revised: The recognition network q r ( z | X ) does not need additional training.

Original: We utilize the BERT intent classifier mentioned above, which achieves 87 .
Revised: We utilize the BERT intent classifier mentioned above, which achieves 87 .

Original: 75% accuracy in intent classification.
Revised: 75% accuracy in intent classification.

Original: In addition, as the sample operation easily brings noise for the intent representation learning when sampling a wrong intent, we use argmax operation to avoid the noise, the response reconstruction loss is:
Revised: In addition, as the sample operation easily brings noise for the intent representation learning when sampling a wrong intent, we use argmax operation to avoid the noise, the response reconstruction loss is:

Original: [Equation]
Revised: [Equation]

Original: k ∈ { 0 , 1 , 2 , · · · , 8 } , each integer corresponds to a specific empathetic intent as in Figure 2.
Revised: k ∈ { 0 , 1 , 2 , · · · , 8 } , each integer corresponds to a specific empathetic intent as in Figure 2.

Original: 4. Intent Predictor and Emotion Classifier
Revised: 4. Intent Predictor and Emotion Classifier

Original: The intent predictor is based on the prior network p i ( z | C ) , which predicts the distribution of response intent by the given context.
Revised: The intent predictor is based on the prior network p i ( z | C ) , which predicts the distribution of response intent by the given context.

Original: During inference, we sample potential intents from this distribution in order to generate human-like empathetic responses.
Revised: During inference, we sample potential intents from this distribution in order to generate human-like empathetic responses.

Original: Specifically, the context is encoded with gated recurrent units (GRU) (Chung et al., 2014):
Revised: Specifically, the context is encoded with gated recurrent units (GRU) (Chung et al., [Equation]

Original: [Equation]
Revised: [Equation]

Original: where h t is the hidden state of GRU encoder, E ( c t ) denotes the word embedding of the t -th word in context, we use h m as context embedding, then the prior network is:
Revised: where h t is the hidden state of GRU encoder, E ( c t ) denotes the word embedding of the t -th word in context, we use h m as context embedding, then the prior network is:

Original: [Equation]
Revised: [Equation]

Original: where FFN represents Feed-Forward Network with two layers.
Revised: where FFN represents Feed-Forward Network with two layers.

Original: The prior intent distribution is supervised by recognition distribution with KLdivergence in Eq.1:
Revised: The prior intent distribution is supervised by recognition distribution with KLdivergence in Eq.1:

Original: [Equation]
Revised: [Equation]

Original: Since the context emotion is proved to be beneficial to empathetic dialogue generation (Rashkin et al., 2019; Lin et al., 2019; Majumder et al., 2020), we also employ an emotion classifier to classify the emotionsituation of context:
Revised: Since the context emotion is proved to be beneficial to empathetic dialogue generation (Rashkin et al., 2019; Lin et al., 2019; Majumder et al., 2020), we also employ an emotion classifier to classify the emotion of context:

Original: [Equation]
Revised: [Equation]

Original: Given the ground truth emotion label e t , the emotion classifier is trained with cross-entropy loss:
Revised: Given the ground truth emotion label e t , the emotion classifier is trained with cross-entropy loss:

Original: [Equation]
Revised: [Equation]

Original: 4. Response Generator
Revised: 4. Response Generator

Original: As for the response generation p ( X | C, z ) , we consider implicit intent embedding for the high-levelabstraction of an intent.
Revised: As for the response generation p ( X | C, z ) , we consider implicit intent embedding for the high-level abstraction of an intent.

Original: In addition, we also introduce intent keywords for explicitly utilizing intentknowledge during the generation process.
Revised: In addition, we also introduce intent keywords for explicitly utilizing intent knowledge during the generation process.

Original: Implicit.
Revised: Implicit.

Original: To generate response with an empathetic intent, the most intuitive approach is takingthe intent embedding as additional input to decoderduring the generation process.
Revised: To generate response with an empathetic intent, the most intuitive approach is taking the intent embedding as additional input to decoder during the generation process.

Original: We also consideremotion embedding as traditional empathetic dialogue models:
Revised: We also consider emotion embedding as traditional empathetic dialogue models:

Original: [Equation]
Revised: [Equation]

Original: where s t is the state of GRU decoder, c att denotesthe context attention value which contains key information of context (Bahdanau et al., 2015).
Revised: where s t is the state of GRU decoder, c att denotes the context attention value which contains key information of context (Bahdanau et al., 2015).

Original: v ( z )is intent embedding and v ( e ) is emotion embedding, both will not change during the generationprocess.
Revised: v ( z ) is intent embedding and v ( e ) is emotion embedding, both will not change during the generation process.

Original: However, this may sacrifice grammaticalcorrectness (Zhou et al., 2018; Ghosh et al., 2017).
Revised: However, this may sacrifice grammatical correctness (Zhou et al., 2018; Ghosh et al., 2017).

Original: Therefore we add a gate operation to capture intentand emotion dynamically:
Revised: Therefore we add a gate operation to capture intent and emotion dynamically:

Original: [Equation]
Revised: [Equation]

Original: where ⊙ represents element-wise product.
Revised: where ⊙ represents element-wise product.

Original: Eachtime step, the intent representation is used appropriately according to current word, state, and contextvalue, the gate operation  is the same for emotion.
Revised: Each time step, the intent representation is used appropriately according to current word, state, and context value. The gate operation for emotion is the same as above.

Original: Explicit.
Revised: Explicit.

Original: The empathetic expression is quitedistinct over vocabularies, e.g., ‘know’, ‘understand’, ‘agree’, are indicative of the empatheticintent Agreeing .
Revised: The empathetic expression is quite distinct over vocabularies, e.g., ‘know’, ‘understand’, ‘agree’, are indicative of the empathetic intent Agreeing .

Original: Therefore, we employ the copymechanism to explicitly utilize intent keywords forintent conditional generation.
Revised: Therefore, we employ the copy mechanism to explicitly utilize intent keywords for intent conditional generation.

Original: See  Appendix formore details about intent keywords .
Revised: See in Appendix B for more details about intent keywords .

Original: [Equation]
Revised: α t = Sigmoid ( v ⊤ s s t ) , p ( x t = w g ) = Softmax ( W g s t ) , p ( x t = w i ) = Softmax ( W i s t ) , p ( x t ) =

Original: 10)where { s t , v s } ∈ R d × 1 , { W g , W i } ∈ R V × d , d ishidden size and V denotes the vocabulary size.
Revised: (1 − α t ) · p ( w g ) + α t · p ( w i ) , (10) where { s t , v s } ∈ R d × 1 , { W g , W i } ∈ R V × d , d is hidden size and V denotes the vocabulary size.

Original: Thecopy rate α t is used to balance the choice betweenintent keywords and generic words, it is trainedwith binary cross entropy loss:
Revised: The copy rate α t is used to balance the choice between intent keywords and generic words, it is trained with binary cross entropy loss:

Original: L 4 = n (cid:88) t =

Original: q t · log α t +(

Original: [Equation]
Revised: [Equation]

Original: q t )

Original: [Equation]

Original: log(

Original: [Equation] t [Equation] , (11) n is the word number of  response, q t ∈ { 0 , 1 } is the truth whetherthe response word x t is  intent keyword.
Revised: n is the amount of words in response, q t ∈ { 0 , 1 } indicates that whether x t is a intent keyword.

Original: 4. Loss Function
Revised: 4. Loss Function

Original: To summarize, the total loss is:
Revised: To summarize, the total loss is:

Original: [Equation]
Revised: [Equation]

Original:  where λ is the hyper-parameter controlling the proportion of its part.
Revised: In order to join all losses with weighting method, we add 4 hyperparameters in total loss, lambda i , where each lambda i is corresponding to L i .

Original: L 1 , L 2 , L 3 , L 4 denote the losses of response reconstruction, intent prediction, emotion classification and copy rate prediction respectively.
Revised: L 1 , L 2 , L 3 , L 4 denote the losses of response reconstruction, intent prediction, emotion classification and copy rate prediction respectively.

Original:  Experiments 
Revised:  Experiments 5. Dataset

Original: 5. Dataset

Original: We evaluate our method and compare with others on EmpatheticDialogues 2 (Rashkin et al., 2019) which contains 25 k open domain dialogues.
Revised: We evaluate our method and compare with others on EmpatheticDialogues 2 (Rashkin et al., 2019) which contains 25 k open domain dialogues.

Original: Follow the same setting as the authors of this dataset, the proportion of train/validation/test data is 8 : 1 : 1 .
Revised: Follow the same setting as the authors of this dataset, the proportion of train/validation/test data is 8 : 1 : 1 .

Original: Each dialogue consists of at least two utterances between a speaker and listener.
Revised: Each dialogue consists of at least two utterances between a speaker and listener.

Original: There are 32 emotion situations in total, which are uniformly distributed.
Revised: There are 32 emotion situations in total, which are uniformly distributed.

Original: 5. Baselines
Revised: 5. Baselines

Original: We compare our model with the three latest empathetic conversational models:
Revised: We compare our model with the three latest empathetic conversational models:

Original: • Multitask Transformer (Multi-TRS).
Revised: • Multitask Transformer (Multi-TRS).

Original: A transformer model trained by the response generation task and the context emotion clas- sification task (Rashkin et al., 2019).
Revised: A transformer model trained by the response generation task and the context emotion classification task (Rashkin et al., 2019).

Original: • Mixture of Empathetic Listeners (MoEL).
Revised: • Mixture of Empathetic Listeners (MoEL).

Original: An enhanced transformer model with emotion-specific decoders to respond appro- priately for each emotion (Lin et al., 2019).
Revised: An enhanced transformer model withemotion-specific decoders to respond appropriately for each emotion (Lin et al., 2019).

Original: • MIMicking Emotions for Empathetic Re- sponse Generation (MIME).
Revised: • MIMicking Emotions for Empathetic Response Generation (MIME).

Original: The state-of-

Original: the-art empathetic dialogue model allows the generator to mimic the context emotion to a varying degree based on its positivity, negativ- ity, and content.
Revised: The state-ofthe-art empathetic dialogue model allows the generator to mimic the context emotion to a varying degree based on its positivity, negativity, and content.

Original: Furthermore, they introduce stochasticity into the emotion mixture and achieves one-to-many generation (Majumder et al., 2020).
Revised: Furthermore, they introduce stochasticity into the emotion mixture and achieves one-to-many generation (Majumder et al., 2020).

Original: 5. Evaluation5.3. Automatic Metrics
Revised: 5. Evaluation 5.3. Automatic Metrics

Original: • BLEU.
Revised: • BLEU.

Original: We choose BLEU (Papineni et al.

Original:  for relevance evaluation which mea- sures the n -gram overlaps with referenceand compute BLEU scores for n ≤ 4 us- ing smoothing techniques (Chen and Cherry,2014).
Revised: We choose BLEU (Papineni et al., 2002) for relevance evaluation which measures the n -gram overlaps with reference and compute BLEU scores for n ≤ 4 using smoothing techniques (Chen and Cherry, 2014).

Original: Since the state-of-art model MIMEand ours are both one-to-many generators,we calculate BLEU recall and BLEU preci- sion (Zhao et al., 2017; Gu et al., 2019).For
Revised: Since the state-of-art model MIME and ours are both one-to-many generators, we calculate BLEU recall and BLEU precision (Zhao et al., 2017; Gu et al., 2019).

Original:  each test case, we sample 5 responses from latent space and use greedy search for MIME and EmpHi, use beam search for MoEL and Multitask-Transformer.
Revised: For each test case, we sample 5 responses from latent space and use greedy search for MIME and EmpHi, use beam search for MoEL and Multitask-Transformer.

Original: • Distinct.
Revised: • Distinct.

Original: Distinct (Li et al., 2016) is a widely used metric for diversity evaluation.
Revised: Distinct (Li et al., 2016) is a widely used metric for diversity evaluation.

Original: Specifi- cally, we compute the number of distinct unigrams (Distinct-1) and bigrams (Distinct-2),then scale them by the total number of uni- grams and bigrams.
Revised: Specifically, we compute the number of distinct unigrams (Distinct-1) and bigrams (Distinct-2), then scale them by the total number of unigrams and bigrams.

Original: 5.3. Human Ratings
Revised: 5.3. Human Ratings

Original: First, we randomly sample 100 dialogues and theircorresponding generations from the three baselinemodels and EmpHi.
Revised: First, we randomly sample 100 dialogues and their corresponding generations from the three baseline models and EmpHi.

Original: Then, we invite five volunteerswith master degrees to do the human evaluation.
Revised: Then we invite five volunteers with master degrees to do the human evaluation

Original: The annotators mark each response from 1 to 5 forempathy, relevance, and fluency.
Revised: The annotators mark each response from 1 to 5 for empathy, relevance and fluency.

Original: To clarify the marking criteria, we provide anexplanation for each metric:
Revised: To clarify the marking criteria we provide an explanation for each metric:

Original: • Empathy.
Revised: • Empathy.

Original: Whether the response shows that the listener understands and shares the speaker’s feeling.
Revised: Whether the response shows that the listener understands and shares the speaker’s feeling.

Original: Can the listener imagine what it would be like in the speaker’s situa- tion?
Revised: Can the listener imagine what it would be like in the speaker’s situation?

Original: • Relevance.
Revised: • Relevance.

Original: Whether the response is relevant to the context.
Revised: Whether the response is relevant to the context.

Original: • Fluency.
Revised: • Fluency.

Original: Whether the response is easy to read and grammatically correct.
Revised: Whether the response is easy to read and grammatically correct.

Original: 5.3. Human A/B Test
Revised: 5.3. Human A/B Test

Original: Following (Lin et al., 2019; Majumder et al., 2020),we construct this evaluation task to directly compare our model with each baseline.
Revised: Following (Lin et al., 2019; Majumder et al., 2020), we construct this evaluation task to directly compare our model with each baseline.

Original: We randomlysample 100 dialogue responses from EmpHi vs { Multitask-Trans , MoEL , MIME }.
Revised: We randomly sample 100 dialogue responses from EmpHi vs { Multitask-Trans , MoEL , MIME }.

Original: Given randomly ordered responses from above models, four annotators select the better response, or tie if they think the two responses have the same quality.
Revised: Given randomly ordered responses from above models, four annotators select the better response, or tie if they think the two responses have the same quality.

Original: The average score of four results is calculated, and shown in Table 6.
Revised: The average score of four results is calculated, and shown in Table 6.

Original: 5. Implement Detail
Revised: 5. Implement Detail

Original: For MIME 3 (Majumder et al., 2020) and MoEL (Lin et al., 2019), we reproduce their results using their open-source codes and their default hyperparameters.
Revised: For MIME 3 (Majumder et al., 2020) and MoEL(Lin et al., 2019), we reproduce their results using their open-source codes and their default hyperparameters.

Original: According to the log-likelihood in the validation dataset for Multitask-Transformer, we use grid search for the best head number, layer number, and feed-forward neural network size.
Revised: According to the log-likelihood in the validation dataset for Multitask-Transformer, we use grid search for the best head number, layer number, and feed-forward neural network size.

Original: The best set is 2, 10, and 256, respectively.
Revised: The best set is 2, 10, and 256, respectively.

Original: EmpHi uses a two-layer Bi-GRU as the encoder and a two-layer
Revised: EmpHi uses a two-layer Bi-GRU as the encoder and a two-layer

Original: GRU as the decoder, λ is set as [1 , 0 . 5 , 0 . 5 , 1] respectively.
Revised: GRU as the decoder, λ is set as [1 , 0 . 5 , 0 . 5 , 1] respectively.

Original: All the feed-forward neural networks in EmpHi have two layers, 300 hidden units and
Revised: All the feed-forward neural networks in EmpHi have two layers, 300 hidden units and

Original: ReLU activations.
Revised: ReLU activations.

Original: For the sake of fairness, we use pretrained Glove vectors (Pennington et al., 2014) with 300 dimensions as the word embedding for all models, the batch size is 16 , and the learning rate is set to 1 e − 4 .
Revised: For the sake of fairness, we use pretrained Glove vectors (Pennington et al., 2014) with 300 dimensions as the word embedding for all models, the batch size is 16 , and the learning rate is set to 1 e − 4 .

Original:  Results and Discussions 
Revised:  Results and Discussions 6. Results Analysis

Original: 6. Results Analysis

Original: In this section, we mainly testify:
Revised: In this section, we mainly testify:

Original: • human-like empathetic intent boost EmpHi’s performance in terms of empathy, relevance, and diversity.
Revised: • human-like empathetic intent boost EmpHi’s performance in terms of empathy, relevance, and diversity.

Original: • EmpHi successfully captures the empathetic intent distribution of humans.
Revised: • EmpHi successfully captures the empathetic intent distribution of humans.

Original: 6.1. Human Evaluation
Revised: 6.1. Human Evaluation

Original: As shown in Table 1, EmpHi outperforms all baselines in terms of empathy, relevance, and fluency.
Revised: As shown in Table 1, EmpHi outperforms all baselines in terms of empathy, relevance, and fluency.

Original: The most distinct improvement is 15.1% on relevance because our model does not only depends on the speakers’ emotion, but also makes use of the empathetic intents, which are contextually relevant.
Revised: The most distinct improvement is 15.1% on relevance because our model does not only depends on the speakers’ emotion, but also makes use of the empathetic intents, which are contextually relevant.

Original: It is worth noting that empathy is the primarymetric in empathetic dialogue generation.
Revised: It is worth noting that empathy is the primary

Original: EmpHi outperforms the previous SOTA on empathy by 9.43%, which directly indicates that human-like empathetic intents are beneficial to the empathy

Original: [Table] [Table]
Revised: [Table] [Table]

Original: ability of the dialogue model.
Revised: metric in empathetic dialogue generation.

Revised: EmpHi outperforms the previous SOTA on empathy by 9.43%, which directly indicates that human-like empathetic intents are beneficial to the empathy ability of the dialogue model.

Original: Last but not least,a decent fluency score proves that our generatedresponse could be understood by humans easily,where our model shows an improvement of 9.87%.
Revised: Last but not least, a decent fluency score proves that our generated response could be understood by humans easily, where our model has an improvement of 9.87% from MoEL.

Original: In addition, the human A/B test results in Table2 also confirm that the responses from our modelare preferable to baselines.
Revised: In addition, the human A/B test results in Table 2 also confirm that the responses from our model are preferable to baselines.

Original: Overall, EmpHi successfully generates empathetic, relevant, and fluentresponses.
Revised: Overall, EmpHi successfully generates empathetic, relevant, and fluent responses.

Original: 6.1. Automatic Evaluation
Revised: 6.1. Automatic Evaluation

Original: As seen in Table 1, the automatic evaluation is consistent with human evaluation.
Revised: As seen in Table 1, the automatic evaluation is consistent with human evaluation.

Original: The BLEU recalland F1 score are improved by 14 . 2% and 8 .
Revised: The BLEU recall and F1 score are improved by 14 . 2% and 8 .

Original: 34% ,respectively.
Revised: 34% , respectively.

Original: However, we only have a slight improvement on BLEU precision, which is similarto (Zhao et al., 2017; Gu et al., 2019) because theprecision is penalized when the model generatesdiverse responses.
Revised: However, we only have a slight im- [Table] provement on BLEU precision, which is similar to (Zhao et al., 2017; Gu et al., 2019) because the precision is penalized when the model generates diverse responses.

Original: Also, the distinct value of uni- [Table] grams and bigrams arehigher, about 32 .
Revised: Also, the distinct value of unigrams and bigrams are 32 .

Original: 04% and 19 .
Revised: 04% and 19 .

Original: 32%  than the previous SOTA, respectively.
Revised: 32% higher than the previous SOTA, respectively.

Original: As shown in Figure 4 and Figure 6, the empathy intents of EmpHi’s responses are more diverse than[Table] existing models, so the distinct scores improve significantly.
Revised: As shown in Figure 4 and Figure 6, the empathy intents of EmpHi’s responses are more diverse than existing models, so the distinct scores improve significantly.

Original: It should be noticed that our method enhances the relevance and diversity simultaneously, which proves the effectiveness of human-like intent in empathetic response generation.
Revised: Our method enhances the relevance and diversity simultaneously, which proves the effectiveness of human-like intent in empathetic response generation.

Original: 6.1. Empathetic Intent Distribution
Revised: 6.1. Empathetic Intent Distribution

Original: We apply the same approach in Section 3 and quantify the empathetic intent distribution of EmpHi’s responses to prove that EmpHi accurately captures humans’ empathetic intent distribution.
Revised: We apply the same approach in Section 3 and quantify the empathetic intent distribution of EmpHi’s responses to prove that EmpHi accurately captures humans’ empathetic intent distribution.

Original: Comparing
Revised: Comparing

Original: Figure 4 and Figure 6, the difference between them illustrates that our model successfully reduces the bias of empathetic expression.
Revised: Figure 4 and Figure 6, the difference between them illustrates that our model successfully reduces the bias of empathetic expression.

Original: The KL-divergence of intent distributions between models and humans are 0.025 for EmpHi, 1 . 949 for MIME, 1 . 545 for
Revised: The KL-divergence of intent distributions between models and humans are 0.025 for EmpHi, 1 . 949 for MIME, 1 . 545 for

Original: MoEL, and 4 .
Revised: MoEL, and 4 .

Original: 570 for Multitask-Transformer (See in Appendix).
Revised: 570 for Multitask-Transformer (See in Appendix A).

Original: 6. Ablation Study
Revised: 6. Ablation Study

Original: We evaluate each part of EmpHi according to
Revised: We evaluate each component of EmpHi using

Original: BLEU and ACC, where ACC indicates the accuracy of whether the empathetic intent of generated response is the same as ground truth.
Revised: BLEU and ACC, where ACC indicates the accuracy of predicted empathethetic intent of generated response.

Original: This value is low because one context could have multiple empathetic responses with various empathetic intents. As seen in Table 3, there is a dramatic drop in the performance of EmpHi without any intent information (both implicit embedding and explicit keywords). Therefore, this proves the effectiveness [Table]
Revised: Since each conversation could have multiple empathetic responses, the ACC of 26.8% is pretty ideal.

Original: and i [Table] [Table] ended
Revised: As seen in Table 3, there is a dra-

Original: [Table]
Revised: [Table]

Original: lasting like

Original: [Table]
Revised: [Table]

Original: I

Original: [Table]

Original: fine [Table] I [Table]
Revised: matic drop in the performance of EmpHi without any intent information (both implicit embedding and explicit keywords).

Original: [Table] [Table] of empathetic intents and the intent representationlearning approach.
Revised: Therefore, this proves the effectiveness of empathetic intents and the intent representation learning approach.

Original: As for implicit gate control,we can tell that it improves both response qualityand intent accuracy because it helps dynamicallycapture intent information during generation.
Revised: As for implicit gate control, it improves both response quality and intent accuracy since it helps EmpHi dynamically capture intent information during generation.

Original: Thisresult is the same as (Zhou et al., 2018).
Revised: Same conclusion has been made in (Zhou et al., 2018).

Original: The copymechanism provides EmpHi the ability to explicitly use intent keywords and thus contributes to theintent accuracy.
Revised: The copy mechanism provides EmpHi the ability to explicitly use intent keywords and thus contributes to the intent accuracy.

Original: 6. Case Study
Revised: 6. Case Study

Original: Intent-level diverse generation.
Revised: Intent-level diverse generation.

Original: Through sampling intents in the discrete latent space, EmpHigenerates different responses with empathetic intents.
Revised: Through sampling intents in the discrete latent space, EmpHi generates different responses with empathetic intents.

Original: As in Figure 7, the speaker shows an exciting emotion forthe opportunity of getting a betterjob.
Revised: As in Figure 7, the speaker shows an exciting emotion for getting a better job.

Original: EmpHi generates empathetic yet contextually relevant responses as humans.
Revised: EmpHi generates empathetic yet contextually relevant responses as humans.

Original: Besides, EmpHi predicts the potential intent distribution andshows successful conditional generation based on the corresponding intents, which improves the interpretability and controllability of empathetic response generation.
Revised: Besides, EmpHi predicts the potential intent distribution and shows successful conditional generation based on the corresponding intents, which improves the interpretability and controllability of empathetic response generation.

Original: We also have an error analysis for a more comprehensive understanding of EmpHi in the Appendix.
Revised: See Appendix C for error analysis.

Original: Compare with existing models.
Revised: Compare with existing models.

Original: For the first instance in Table 4, even though baseline models show naive empathy in their response, it is hard for the speaker to feel empathy because the response is not relevant to the topic.
Revised: For the first instance in Table 4, even though baseline models show naive empathy in their response, it is hard for the speaker to feel empathy because the response is not relevant to the topic.

Original: In contrast, EmpHi shows its understanding of the speaker’s feelings and asks a relevant question to explore the speaker’s experience.
Revised: In contrast, EmpHi shows its understanding of the speaker’s feelings and asks a relevant question to explore the speaker’s experience.

Original: The second case tells the same story. Again, all baselines express contextually irrelevant empathy, whereas EmpHi truly understands the scene and  further reply: "Maybe you should go to the police" with the Suggesting intent.
Revised: For second case, all baselines express contextually irrelevant empathy, while EmpHi truly understands the dialogue history and put itself into speaker’s situation, then further reply: "Maybe you should go to the police" with the Suggesting intent.

Original:  Conclusion
Revised:  Conclusion

Original: Overall, we reveal the severe bias of empathetic expression between existing dialogue models and humans.
Revised: Overall, we reveal the severe bias of empathetic expression between existing dialogue models and humans.

Original: To address this issue, this paper proposes EmpHi to generate empathetic responses with human-like empathetic intents.
Revised: To address this issue, this paper proposes EmpHi to generate empathetic responses with human-like empathetic intents.

Original: As a result, both automatic and human evaluation prove that
Revised: As a result, both automatic and human evaluation prove that

Original: EmpHi has a huge improvement on empathetic conversation.
Revised: EmpHi has a huge improvement on empathetic conversation.

Original: According to the anlaysis and case studies, EmpHi successfully learns to be empathetic consistently with humans and shows high interpretability  during the generation process.
Revised: According to the anlaysis and case studies, EmpHi successfully learns the emapthetic intent distribution of human and shows high interpretability and controllability during the generation process.

Original: We willadd more empathetic intents, e.g., delighting, cheering, persuading, etc, and try large pretrained  models  in our future work.
Revised: We will try large pretrained language models with empathetic intent in our future work.

Original: Ethical Statement
Revised: Ethical Statement

Original: Since this paper involves subjects related to humanconversation, we have ensured that all the experiments will cause no harm to humans.
Revised: Since this paper involves subjects related to human conversation, we have ensured that all the experiments will cause no harm to humans.

Original: The dataset
Revised: The dataset

Original: EmpatheticDialogues is collected by (Rashkinet al., 2019), all the participants join the data collection voluntarily.
Revised: EmpatheticDialogues is collected by (Rashkin et al., 2019), all the participants join the data collection voluntarily.

Original: Also, the dataset provider filtersall personal information and obscene languages.
Revised: Also, the dataset provider filters all personal information and obscene languages.

Original: Therefore, we believe that the dataset EmpatheticDialogues used in our experiments are harmless tousers, and the model trained on this dataset is notdangerous to humans. 
Revised: Therefore, we believe that the dataset EmpatheticDialogues used in our experiments are harmless to users and the model trained on this dataset is not dangerous to humans. Acknowledgements

Revised: This research was supported in part by the National Key Research and Development Program of China (No. 2020YFB1708200), the Guangdong

Revised: Basic and Applied Basic Research Foundation (No.

Revised: 2019A1515011387) and the Shenzhen Key Laboratory of Marine IntelliSense and Computation under

Revised: Contract ZDSYS20200811142605016.

Revised: A Empathetic Expression Gap

Revised: For more comprehensive recognization of the severe emathy expression bias between existing empathetic dialogue models and humans, we further quantify the bias of Multitask-Transformer (Rashkin et al., 2019) in Figure 8 and MoEL (Lin et al., 2019) in Figure 9, the intent index is consistent with Figure 2.

Revised: The results are similar with

Revised: MIME (Majumder et al., 2020), we can see the large intent distribution bias and the monotony of empathetic expression of existing models.

Revised: B Intent Keywords Collection

Revised: The keywords are retrieved from the training set of Empathetic Intents dataset (Welivita and Pu, 2020) by using TF-IDF method.

Revised: Empathetic Intents has a training set of 5490 responses, where each intent group has 610 responses.

Revised: Based on the labeled intent for each response in the training set, we concatenate all the responses which are in the same group and remove all the stop words.

Revised: Finally, we apply TF-IDF to obtain the top k keywords for each intent group, we set k to 30 in our experiments.

Revised: See Table 5 for top ten keywords for each intent.

Revised: C Error Analysis

Revised: Although EmpHi achieves huge improvement in terms of empathy, relevance, and diversity in empathetic dialogue generation, there is still some flaws.

Revised: At first, the generation task of EmpHi is far difficult than existing models, because it needs to generate response condition on both context and the predicted intent, while other models generate response only condition on the context, therefor the exposure bias of EmpHi is more severe.

Revised: See in Table 6, although the predicted intent of EmpHi is the same as reference and its corresponding response is great, EmpHi also gives high probability for Questioning intent and the corresponding response is not very contextually relevant, EmpHi knows it is suitable for asking more details to show its caring, but it does not know how to ask under this context, thus EmpHi needs better understanding for context information.

Revised: We believe this issue could be mitigated when using more dialogue data for pretraining.

Revised: [Table]

Revised: Agreeing know, understand, agree, definitely, feel, feeling, exactly, mean, oh, right

Revised: Acknowledging sounds, nice, awesome, like, great, cool, would, oh, must, really

Revised: Encouraging hope, well, hopefully, get, good, time, bet, great, goes, soon

Revised: Consoling hope, hopefully, get, better, well, soon, time, find, good, things

Revised: Sympathizing sorry, hear, oh, am, happened, loss, feel, hope, really, aw

Revised: Suggesting maybe, get, time, could, think, well, next, something, try, go

Revised: Questioning oh, get, go, going, long, kind, like, work,

Revised: good, maybe

Revised: Wishing congratulations, luck, good, wish, best, well, happy, oh, birthday, wow

Revised: Neutral good, like, sure, well, time, one, have, people, never, get

Revised: [Table]

Revised: Context

Revised: My wife was able to get a job with microsoft without going to a four-year college!

Revised: Reference

Revised: That’ s pretty amazing, she must be skillful regardless.

Revised: Intent

Revised: Prob

Revised: Generated Response

Revised: Agreeing 2.91%

Revised: Oh, wow !

Revised: That’s amazing !

Revised: I know how you feel.

Revised: Acknowledging 40.9%

Revised: That’s awesome !

Revised: I’m glad you’re going to be proud of her.

Revised: Encouraging 9.98%

Revised: That’s awesome !

Revised: I hope you guys have a great job!

Revised: Consoling 3.66%

Revised: Wow, That’ s good, i hope you are able to get it .

Revised: Sympathizing 1.43%

Revised: Oh wow !

Revised: I am sorry to hear that.

Revised: Suggesting 0.62%

Revised: That’s good !

Revised: Maybe she will be fine.

Revised: Questioning 28.6%

Revised: That’s great !

Revised: How long have you been working ?

Revised: Wishing 9.19%

Revised: Congratulations !

Revised: That’ s awesome !

Revised: congratulations !

Revised: Neutral 2.75%

Revised: That is a good thing to do, I wish i was a lot of work .