Emotion and Sentiment Guided Paraphrasing
Year: 2023

Abstract:
Paraphrase generation, a.k.a. paraphrasing, is a common and important task in natural language processing. Emotional paraphrasing, which changes the emotion embodied in a piece of text while preserving its meaning, has many potential applications, including moderating online dialogues and preventing cyberbullying. We introduce a new task of fine-grained emotional paraphrasing along emotion gradients, that is, altering the emotional intensities of the paraphrases in fine-grained settings following smooth variations in affective dimensions while preserving the meaning of the original text. We reconstruct several widely used paraphrasing datasets by augmenting the input and target texts with their fine-grained emotion labels. Then, we propose a framework for emotion and sentiment guided paraphrasing by leveraging pre-trained language models for conditioned text generation. Extensive evaluation of the fine-tuned models suggests that including fine-grained emotion labels in the paraphrase task significantly improves the likelihood of obtaining high-quality paraphrases that reflect the desired emotions while achieving consistently better scores in paraphrase metrics such as BLEU, ROUGE, and METEOR.

heading: 1 Introduction
text: With the rise of social media and online chat rooms, the textual aspect of language is often found to be the only aspect of communication that is transferred over the Internet. Devoid of any intonations or accompanying facial movements, it is more challenging for people to decipher the true meaning and underlying emotion that a message is intended to convey, especially if that message incorporates the more complex aspects of speech. This could lead to negative social consequences. For example, political tweets from prominent figures without
∗Work done as a research intern at Portland State University.
careful consideration can lead to political radicalization and conflicts. Furthermore, on messaging apps such as Discord, cyberbullies attack others with emotion-ladened words while innocent people send unnecessarily emotional messages in the heat of the moment. Emotional paraphrasing could be an important solution to overly intense emotions expressed on social media (Seehausen et al., 2012) and provide support toward moderation of hate speech (Tontodimamma et al., 2021; Altarawneh et al., 2023).
Paraphrase generation (a.k.a. paraphrasing), a key task in natural language processing, involves generating an output text that preserves the meanings of the input text while including variations in words and grammars. The refined task of emotional paraphrasing has garnered much recent attention (Casas et al., 2021). Its goal is to alter the underlying emotion associated with a sentence while maintaining its meaning.
In this paper, we introduce a new task of finegrained emotional paraphrasing along emotion gradients, i.e., altering emotional intensities in fine grain following smooth variations in affective dimensions (e.g., from anger to annoyance) while preserving the overall meaning. First, we analyze and reconstruct existing paraphrasing datasets to adapt them for the current task. Next, we propose the concept of an emotion-transition graph where transitions are based on the fine-grained emotions and their emotion gradients as identified by GoEmotions (Demszky et al., 2020), and are constrained by specific goals of emotion transition. Then, we develop a framework for emotion and sentiment guided paraphrasing by leveraging several pretrained language models for conditioned text generation under zero-shot, few-shot, and fully supervised settings. Lastly, we conduct a comprehensive evaluation of the proposed framework with several datasets using metrics pertaining to both paraphrasing and emotion transition.
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In all settings, the fully supervised and fewshot fine-tuned models showed significant improvements over the zero-shot base models, i.e., doubling the number of exact matches of desired fine-grained emotions while achieving consistently better scores in paraphrase metrics such as BLEU, ROUGE, and METEOR. Few-shot learning delivered competitive performances in all categories compared with fully-supervised. This study indicates that our finegrained emotional paraphrasing framework has potentials in applications to specific scenarios, e.g., chat rooms, forums, and public online spaces.
Specifically, our contributions include:
• Reconstructed Emotion Paraphrase Datasets: Given existing paraphrase datasets, we apply a fine-grained emotion classification model to label the input text and target text of each paraphrase pair with their emotions (see examples in Table 1). A similar procedure is also applied to label each paraphrase pair with their sentiment intensities: neutral, low, or high.
• Emotional Paraphrasing Models: Leveraging pre-trained language models, we propose a paraphrasing framework guided by emotion and sentiment transitions.
• Evaluation: We conduct an extensive set of experiments to verify the effectiveness of the proposed approach.

heading: 2 Related Work
text: This section discusses two main threads of related work: emotion classification and paraphrasing.

heading: 2.1 Emotion Psychology and Classification
text: Emotions are a key component of human psychology, playing a role in many cognitive processes including learning, memory, decision making, and interpersonal communication (Oatley and Duncan, 1994; Tyng et al., 2017). Equally important is the role that emotions play in human-to-human interactions. Words can trigger emotional responses, both negative and positive. Without facial expressions, vocal intonations, or hand gestures, it is harder to communicate one’s emotions online. The intensities of words can be higher than what someone wants them to communicate. For example, someone could want to communicate frustration, but instead could come off as furious. Rooted in the psychology of communication and emotion, the need for lowering intensity of online communications inspires the task of fine-grained emotional paraphrasing.
In 1890, James et al. proposed fear, grief, love, and rage as a set of the most basic emotions. Then, Plutchik (1980) introduced eight categories of emotions, which was followed by Ekman (1992) who introduced his famous set of six basic emotions: fear, anger, joy, sadness, disgust, and surprise. These taxonomies form the basis of many early NLP experiments pertaining to emotions (Mohammad and Turney, 2010; Agrawal and An, 2012). Another classification produced by Lazarus and Lazarus (1994) included a list of 15 emotions. Recently a study done by Cowen and Keltner (2017) expanded on these classifications. By having human test subjects report on the emotions they felt while viewing videos, the study found that there
were 27 emotion categories, in addition to a neutral emotion. This study also grouped these emotions into “clusters.” Demszky et al. (2020) produced a similar set of 28 emotions that was used in the GoEmotions project. This project provided a labeled dataset of 58K texts and a model based on BERT (Devlin et al., 2018) capable of classifying inputs into one of the 28 emotions. In addition, the GoEmotions project provided a heatmap showing the adjacency between emotions by continuous gradients as well as including a stratification of the emotions into groups (see Appendix A). While the proposed approach can adopt any emotion taxonomy, our work follows the GoEmotions groups as guidance for structuring the proposed emotion transition graph.

heading: 2.2 Paraphrasing
text: Paraphrasing involves changing the wording of an input text while preserving its original meaning. Several stuides combine deep generative models with other modeling and training techniques: e.g., variations using reinforcement learning (Li et al., 2017), long short-term memory or LSTM (Gupta et al., 2018), and stacked residual LSTM (Prakash et al., 2016). Transformer-based text-to-text models such as BART (Lewis et al., 2019) and T5 (Raffel et al., 2020) have become more popular for paraphrasing. Several studies have been conducted to improve these models’ paraphrasing performance through combining Transformers and sequence-tosequence models (Egonmwan and Chali, 2019) and joint paraphrase learning (Min et al., 2020).
Emotional paraphrasing, a task that alters the underlying emotion associated with the input sentence while maintaining its meaning, has been closely studied. Casas et al. (2021) fine-tuned six GPT models (one for each emotion) for emotional paraphrasing, where the input text was paraphrased to fit one of Ekman’s six emotional categories. Our new task, instead, stipulates a more fine-grained emotion categorization and paraphrasing. Our finetuned language models conduct emotional transitions based on the emotion of the input text, and is capable of transitioning to various emotions along emotion gradients on a transition graph.
Our task is also related to emotion or sentiment text style transfer. Sundararaman et al. (2020) proposed an unsupervised aspect-level approach to sentiment controllable style transfer. Other studies include a delete-retrieve-generate approach (Li
et al., 2018) and a mask-infill approach (Wu et al., 2019) to sentiment style transfer. Through masked language modeling and transfer learning, MohammadiBaghmolaei and Ahmadi (2023) adapted style transfer to transform texts into one of four emotions: anger, fear, sadness, and joy. While these tasks transfer text following certain emotion or sentiment styles, our task focuses on more flexible fine-grained emotion and sentiment transitions.
As our task lowers emotion intensity of input texts, thereby lowering the strong psychological effects that intense emotional interactions can bring, it also relates to the task of positive reframing (Ziems et al., 2022). Both focus on altering the emotions of texts, while preserving its underlying connotations. However, the task of positive reframing emphasizes altering the input text into a positive emotion while our task does not transit every emotion into a positive one, but rather lowers the intensities of emotions, which allows negative and positive emotions alike. Our goal of lowering the intensity of emotion in text is related to, but different from the task of neutralizing bias (Pryzant et al., 2020). Neutralizing bias strives to eliminate all bias, which results in most paraphrased texts being classified as neutral. Our task aims to preserve the base meaning and tone while lowering the intensity of the emotion in the input text. Thus, the paraphrase still expresses its original view or belief, but in a less provocative or intense manner.

heading: 3 Fine-Grained Emotional Paraphrasing

heading: 3.1 Problem Description
text: Given an input text ti with emotion ei where ei belongs to an emotion adjacency group E : ei ∈ E , the task of fine-grained emotional paraphrasing along emotion gradients is to paraphrase ti into tf where the emotion of tf is ef and (ef ∈ E) ∩ (ei! = ef ). Further constraints help to guide the emotion transitions along a specific affective dimension, e.g., lowering the sentiment intensity. If the intensity of ei is si and that of ef is sf , the refined condition is (ef ∈ E) ∩ (ei! = ef ) ∩ (sf < si).
To tackle the task of fine-grained emotional paraphrasing along emotion gradients, we propose a novel framework as illustrated in Figure 1. The top part of this workflow fine-tunes pre-trained language models into fine-grained emotional paraphrasers. First, it labels the emotions of input and target texts of each paraphrase pair in both train and test sets. Then for each pair, a prefix of the
form "(input emotion) to (target emotion)" is generated. Finally, the train/test sets augmented with emotion transition prefixes are utilized to fine-tune language models, e.g., T5, BART, and GPT-2, under three settings: zero-shot, few-shot, and fully supervised. The bottom of this workflow utilizes the fine-tuned paraphrasing models in inference applications. Given an input text ti, it first identifies the emotion ei of ti. Then it selects a target emotion ef for paraphrasing, utilizing an emotion transition graph that is based on emotion gradients. After that, it generates a prefix for the selected emotion transition "ei to ef ". Finally, it sends the query, "ei to ef : ti" to our fine-tuned paraphraser to generate the target paraphrase tf .

heading: 3.2 Emotion Classification
text: The first step in our workflow is to identify the emotion (ei) of the input text (ti). This is done through our enhanced version of the GoEmotions model: we modified the model to only report the dominant emotion that is above a certain threshold. If no emotion meets the threshold, the model reports no emotion label. Given the input text ti, this classification model identifies the most compatible of the 28 emotions (ef ) to feed into the transition graph. The GoEmotions model has a wider variety and more detailed array of emotions compared to emotion classifications such as Ekman’s. This allows for more precise emotion classifications that enable fine-grained adjustment of paraphrase emotions.

heading: 3.3 Target Emotion Selection Using Emotion Transition Graph
text: The second step in our workflow is target emotion selection using an emotion transition graph such as the one shown in Figure 2. This particular transition graph is intended for lowering sentiment intensity. It is based on the GoEmotions emotion heatmap created by Demszky et al., which
shows emotions as grouped by continuous gradients. Each group of emotions (as shown in Appendix A), although close in sentiments, exhibits different levels of intensities. To measure the sentiment intensities of different emotions, we have applied NLTK’s Vader Score (Hutto and Gilbert, 2014) function to all emotion-labeled texts from the GoEmotions dataset and computed the median score for each emotion (which can be found in Appendix A). Based on the median Vader scores of the 28 emotions, we are able to group them into five groups: high negative, low negative, neutral, low positive, and high positive as shown in Table 2.
The emotion transition graph in Figure 2 is derived by combining the two groupings found in GoEmotions and Table 2. The emotions in red are emotions of high sentiment intensities, positive or negative, those in green are of low sentiment in-
tensities, and those in black have neutral sentiment intensities. The arrows between ovals indicate the emotions in these ovals belong to the same GoEmotions emotion clusters, i.e., they are adjacent and connected with continuous gradients. The arrows to the neutral oval indicate that all emotions can transit to the neutral emotion. By following the transition graph, we can adjust emotion intensity. For example, if the GoEmotions model identifies the input emotion as “anger,” the transition graph may recommend a transition to “annoyance.”

heading: 3.4 Prefix Generation
text: The third step in our workflow is prefix generation. We adopt the multi-task design for text-to-text generation, i.e., many NLP tasks can be cast as textto-text tasks and a prefix can be added to the input text to indicate the task at hand. Our prefix generator utilizes this design and generates the prefix for the task of fined-grained emotional paraphrasing. Given the source emotion ei identified in the emotion classification step and the target emotion ef selected in the target emotion selection step, the prefix is generated in the format of “ei to ef” and placed in front of the input text ti. It guides the fine-tuned language models to paraphrase along the selected emotion transition. An example of such a prefix would be: “anger to disappointment: He is angry to learn that in June Ethan Lovett (Nathan Parsons) is his half brother.”
In addition, we also explore the use of sentiment ranges (i.e., high positive, low positive, neutral, low negative, and high negative) in place of finegrained emotion labels as alternative fine-grained prefixes. Such a prefix would look like: “high_neg to low_neg: He is angry to learn that in June Ethan Lovett (Nathan Parsons) is his half brother.”

heading: 3.5 Paraphrase Generation
text: The final step of our workflow is paraphrase generation which utilizes a fine-tuned language model to complete the task of fine-grained emotion paraphrasing along emotion gradients. Such a model is fine-tuned with a dataset of paraphrase pairs that exemplify the transitions along the continuous gradients that connect the emotions. The fine-tuned model allows for precise emotional paraphrasing by inputting the emotion transition prefix and the original text, paraphrasing it, and outputting the paraphrase that best fits the target emotion.

heading: 4 Experiments
text: Figure 3 illustrates the workflow of our experiments on preparing the train/test datasets for fine-grained emotional paraphrasing, conducting fine-tuning on various language models, and evaluating the emotional paraphrasing performance of these models.
• Given a paraphrase dataset, we first label the input text and target text of each paraphrase pair with fine-grained emotions by using our modified verison of GoEmotions model.
• Second, we remove the paraphrase pairs that have the same input/target emotions and those pairs whose input or target emotions are labeled as neutral, as we are focused on the paraphraser’s ability to lower the emotional intensity instead of neutralizing it.
• Third, we select the paraphrase pairs with decreasing intensity and if a pair has increasing intensity, we flip its input/target texts and emotions, so it can be used in our experiment.
• Fourth, we split the dataset into train/test sets, e.g., with a 80/20 split.
• Fifth, an optional step for few-shot training, we cap the number of instances of the same emotion transition, e.g., 12 in the train set and 3 in the test set following the 80/20 split.
• Sixth, we conduct three types of fine-tuning: fully supervised (or full), zero-shot, and few-shot and compare the performances of each type of fine-tuned model. In the zeroshot case, we directly evaluate the original model without fine-tuning and in the few-shot case, we fine-tune the model with the capped datasets as in Step 5 and evaluate with the full test set.

heading: 4.1 Datasets
text: Three publicly available paraphrasing datasets were used in our experiments after reconstruction. These include Google PAWS-Wiki (PAWS) (Zhang et al., 2019), Microsoft Research Paraphrase Corpus (MRPC) (Dolan and Brockett, 2005), and Quora Questions Pairs (Iyer et al., 2017).
The Google PAWS project produced multiple sets of paraphrasing input-output pairs. We chose to use to PAWS-Wiki Labeled (Final) data because they were generated by translation methods and human verified for accuracy. The MRPC corpus was a compilation of human-annotated data from the news. The Quora corpus has the goal of aiding the training of “semantic equivalence” models, similar to the goals of paraphrasing models. Some sample instances are presented in Table 1.
To make these datasets suitable for our emotional paraphrasing task, we reconstructed them by following Steps 1-4 in Figure 3. The statistics of the filtered datasets are shown in Table 3, and these datasets are also combined into a Mix dataset for the study of overall performance.

heading: 4.2 Evaluation Metrics
text: The emotional paraphrasing capabilities of the models are evaluated from two aspects: emotion transition and paraphrasing.
To evaluate the emotion transition performance of the models, we utilize the Exact metric to com-
pute two scores: Exact-SR and Exact-FE. The Exact-SR score measures the percentage of the emotion sentiment ranges (i.e., high positive, low positive, neutral, low negative, and high negative) of the generated paraphrases that match the target sentiment ranges. The Exact-FE score measures the percentage of the fine-grained emotions of the generated paraphrases that match the target emotions. By comparing the sentiment ranges and specific emotions of the target texts and the predictions of each model, the Exact scores indicate how capable a model is at emotion transitioin.
To evaluate the paraphrasing capabilities of the models, we utilize several metrics: BLEU (Papineni et al., 2002), ROUGE-L (Lin, 2004), and METEOR (Banerjee and Lavie, 2005). They evaluate the similarities of target texts and model predictions.

heading: 4.3 Models
text: Below we discuss our models and training settings. Emotion Labeling. The original GoEmotions model, for each input text, outputs a list of emotions that it identified as being “possible" candidates for the emotion of the input text and a confidence score for each candidate. In our experiments, we modified the model to only report the dominant emotion with a confidence score over 0.5. Paraphrasing. For paraphrasing, we fine-tuned 3 pre-trained language models, T5, BART, and GPT2. We adopted multi-task training. Let ti be the input text and ei be its emotion. Let ef be the target emotion, and tf be the emotional paraphrased output of ti. In the task of fine-grained emotional paraphrasing along emotion gradients, ti, ei, and ef are given to the language model in the query format: “ei to ef : ti”. The fine-tuned model will output tf , a paraphrased version of ti where the underlying semantics of ti is kept and the intensity of emotion is changed. Each model is trained under 3 settings: fully supervised, few-shot, and zero-shot.

heading: 4.4 Implementation
text: We utilized the Simple Transformers package (Rajapakse, 2023) Version 0.63.6 to fine-tune T5 and BART models. For GPT-2, we utilized Huggingface’s transformers implementation (HuggingFace, 2023) Version 4.25.1. We conducted fine-tuning and evaluation on a desktop with an AMD Ryzen 7 5800x, 32GB RAM, and RTX 3080TI GPU. Due to a limited amount of GPU memory, 12GB precisely, we had to adopt a smaller batch size of 6. Each model was fine-tuned over 3 epochs.

heading: 5 Results and Discussions
text: Table 4 summarizes the results from our experiments using T5, BART, and GPT-2 models for the fine-grained emotional paraphrasing task. It can be observed for all three models, fully supervised finetuning significantly outperformed the zero-shot setting in every category in both emotion-transition and paraphrasing metrics. Few-shot fine-tuning delivered competitive performances in all categories compared with the fully supervised setting.
When comparing model performance, it can be observed that T5 outperforms BART and GPT-2 on emotion-transition. This may be attributed to T5’s design as a multi-task model meant to accept the prefixes we utilized. For paraphrasing, BART outclassed both T5 and GPT-2 models in text similarity and consistency. We speculate that designing more appropriate prompts might benefit GPT-2.
In few-shot fine-tuning, we experimented with different limits for the numbers of text pairs following each fine-grained emotion transition in the train/test sets, 4/1, 8/2, 12/3, 16/4, and 20/5 per the 80/20 split. All few-shot train/test sets delivered better emotional transition performance than zeroshot and their paraphrasing performance became consistently better with 12/3 split and above.
One important takeaway from the results is the similarity in performance of using sentiment ranges or fine-grained emotions as part of the prefix prompt to the models. We noticed that there was an insignificant difference in both the emotiontransition and paraphrasing performances of the two prefix types. An explanation for this behavior in the fine-tuned models may be that emotion transitions largely follow continuous gradients among emotions along certain affective dimensions and, therefore, lowering the sentiment intensity from an emotion often transitions to a same target emotion. This means that although the prefixes are different, the models learn the same emotion transitions that are embodied in the paraphrase pairs.
Figure 4 illustrates the success rates of T5 in transitioning texts between different sentiment intensity levels under different fine-tuning settings. We observe that fully supervised and few-shot finetuning both outperform zero-shot significantly in all sentiment intensity lowering transitions. Fully supervised seems to perform better in emotion transitions lowering positive sentiments while few-shot better in lowering negative sentiments. Importantly, we also observe that lowering from high positive or negative to low positive or negative is more challenging for the model than lowering to neutral level.

heading: 6 Case Study on Transition Graph Guided Target Emotion Selection
text: We created a new test dataset from the original Mix test dataset by leveraging transition-graph-guided emotion selection. Instead of utilizing the target emotion provided by the original test dataset, the transition-graph was used to randomly select a new target emotion that would maintain the emotion proximity while lowering the emotional intensity. However, if the neutral emotion was selected, the original target emotion was kept. In doing so, 35 percent of the dataset was given a larger variety of transition types between the high, low, and neutral emotion groups, while the size of the dataset was maintained. The emotion of the model prediction was compared to the desired target emotion to evaluate emotion-transition performance. The model prediction was compared to the original target text for measuring paraphrasing performance.
Figure 5 shows the performances of zero-shot and fully supervised fine-tuned T5 models on this new test dataset. They continue to reflect the observation from Table 4 that the fine-tuned models show major improvements in emotion transition, while maintaining a slight gain in paraphrasing performance. With the increased variety of target emotions, the success rate of the models does decrease as indicated by the lower Exact metrics. This points to the neccesity of paraphrase datasets that provide better coverage of the emotion transi-
tion graph which helps automate the target emotion selection for practical emotion moderation applications.

heading: 7 Conclusions and Future Work
text: In this paper, we introduced a new task of finegrained emotional paraphrasing along emotion gradients. We developed a workflow for addressing this task by fine-tuning pre-trained language models under multi-task learning framework. Our experiments have demonstrated that fine-tuned models perform significantly better than baseline models in both emotion transition and paraphrasing.
For future work, there is still much to improve for fine-grained emotional paraphrasing. We will pursue better datasets for emotional fine-tuning or even develop new datasets for this purpose. We will further develop our approach on top of the state-ofthe-art large language models, e.g., GPT-4. We will also investigate more customized models beyond the baseline language models. For evaluation, we plan to conduct human studies as appropriate.
Limitations
There is no dataset currently available specific for fine-grained emotional paraphrasing. For our study, we have to utilize publicly available paraphrase datasets, Google PAWS, MRPC, and Quora and augment their text pairs with emotions labels. These datasets may not be best suited for study-
ing this new task. Therefore, new datasets that are particularly developed for fine-grained emotional paraphrasing are needed. Furthermore, it is also desirable to evaluate the proposed methods in alternative application scenarios other than lowering sentiment intensity.
When using GoEmotions as our fine-grained emotion classifier, we selected the emotion with the dominant confidence score above the threshold of 0.5. As the authors of GoEmotions have pointed out, there is still much room to improve on the classification accuracy of GoEmotions. Although the confidence score threshold of 0.5 worked well in our experiments, how to set this threshold still requires more studies. Similarly we utilized NLTK’s Vader scores to place emotions into high, low, and neutral intensity groups. The Vader score thresholds for this grouping were selected empirically. Further studies are needed for setting the thresholds or developing better ways for intensity grouping.
In the evaluation of our fine-grained emotional paraphrasing models, we utilized two sets of metrics for emotion transition and paraphrasing respectively. It is desirable to jointly evaluate these two aspects, which we believe would be best done by well-designed human studies in future work.
Ethics Statement
Our study is based on publicly available datasets from reputable sources. The augmented datasets will be made available with open-source code release. The fine-grained emotional paraphraser obtained through our study is based on existing pre-
trained language models and paraphrase datasets; therefore, it may inherit their drawbacks such as undesirable social biases. As an unintended use, the methods proposed by this paper can be utilized or modified to produce paraphrasers that increase the emotional intensities of texts, leading to texts with extreme emotions that can be potentially harmful. While we advocate for voluntary adoption of emotion moderation to achieve more peaceful cyberspaces, we do realize that the proposed methods can be abused as emotion moderation tools for censorship. We strongly oppose such applications.

heading: Acknowledgments
text: We thank the anonymous reviewers for their helpful feedback.

heading: A Appendix

heading: Future Work
text: For future work, there is still much to improve for fine-grained emotional paraphrasing. We will pursue better datasets for emotional fine-tuning or even develop new datasets for this purpose. We will further develop our approach on top of the state-ofthe-art large language models, e. g. , GPT-4. We will also investigate more customized models beyond the baseline language models. For evaluation, we plan to conduct human studies as appropriate. Limitations
There is no dataset currently available specific for fine-grained emotional paraphrasing. For our study, we have to utilize publicly available paraphrase datasets, Google PAWS, MRPC, and Quora and augment their text pairs with emotions labels. These datasets may not be best suited for study-
ing this new task. Therefore, new datasets that are particularly developed for fine-grained emotional paraphrasing are needed. Furthermore, it is also desirable to evaluate the proposed methods in alternative application scenarios other than lowering sentiment intensity. When using GoEmotions as our fine-grained emotion classifier, we selected the emotion with the dominant confidence score above the threshold of 0. 5. As the authors of GoEmotions have pointed out, there is still much room to improve on the classification accuracy of GoEmotions. Although the confidence score threshold of 0. 5 worked well in our experiments, how to set this threshold still requires more studies. Similarly we utilized NLTK’s Vader scores to place emotions into high, low, and neutral intensity groups. The Vader score thresholds for this grouping were selected empirically. Further studies are needed for setting the thresholds or developing better ways for intensity grouping. In the evaluation of our fine-grained emotional paraphrasing models, we utilized two sets of metrics for emotion transition and paraphrasing respectively. It is desirable to jointly evaluate these two aspects, which we believe would be best done by well-designed human studies in future work