Challenge: a human would recognize the emotion of an interlocutor and respond with an appropriate emotion, such as empathy and comfort.
Approach: They propose to build a dialogue corpus annotated with two kinds of emotions . they collect tweets and annotate them with the emotion they put into the utterance .
Outcome: The proposed method shows that it is difficult to recognize experienced emotions and multitask learning is effective.

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Emotional Speech Corpus for Persuasive Dialogue System (2020.lrec-1)

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Challenge: Emotional expressions can be used to express the speaker’s emotion more directly than using only emotion expression in the text.
Approach: They built a speech dialogue corpus in a persuasive scenario that uses emotional expressions to build a system with emotional expression.
Outcome: The proposed system can express the speaker's emotion more directly than using only emotion expression in the text, and the results show that the collected emotional expressions with their speeches have higher emotional expressiveness for expressing the system's emotions to users.
EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators (2020.lrec-1)

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Challenge: Emotion recognition helps to build natural dialogue systems.
Approach: They propose to use a recurrent neural model to annotate emotion corpora with dialogue act labels and an ensemble annotator to extract the final dialogue act label.
Outcome: The proposed model annotates two accessible multi-modal emotion corpora with and without context and extracts the final dialogue act label.
EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems (2022.lrec-1)

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Challenge: Existing emotion-annotated task-oriented corpora are limited in size, label richness, and public availability, creating a bottleneck for downstream tasks.
Approach: They propose a large-scale manually emotion-annotated corpus of task-oriented dialogues based on a multi-domain task-orientated dataset.
Outcome: The proposed method is based on a task-oriented dialogue dataset with 11K dialogues and 83K emotion annotations of user utterances.
Automatic Dialogue Generation with Expressed Emotions (N18-2)

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Challenge: a growing interest in neural dialogue generation systems is focusing on generating human-like responses based on past utterances . despite efforts, few consider putting restrictions on the response itself . authors present three models that concatenate the desired emotion with the source input .
Approach: They propose three models that concatenate the desired emotion with the source input or push the emotion in the decoder.
Outcome: The proposed model is more efficient than the previous models, but it lacks the emotion vector.
EmotionLines: An Emotion Corpus of Multi-Party Conversations (L18-1)

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Challenge: Emotion is a critical characteristic to distinguish people from machines.
Approach: They propose a dataset with emotions labeling on all utterances in each dialogue . they use Friends TV scripts and Facebook messenger dialogues to collect the data .
Outcome: The proposed dataset is the first with emotions labeling on all utterances in each dialogue based on their textual content.
EmoInHindi: A Multi-label Emotion and Intensity Annotated Dataset in Hindi for Emotion Recognition in Dialogues (2022.lrec-1)

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Challenge: Existing datasets for emotion recognition in dialogues are in English . existing datasets are limited to a few languages like Hindi .
Approach: They propose a large conversational dataset in Hindi for multi-label emotion and intensity recognition in conversations . they use a Wizard-of-Oz manner to annotate dialogues with 16 emotion labels .
Outcome: The proposed dataset contains 1,814 dialogues with 44,247 utterances in Hindi . it is based on a Wizard-of-Oz manner and can detect emotions in conversation .
E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation (2023.emnlp-main)

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Challenge: Empathy is a desirable human trait that improves the emotional perceptivity in emotion-bonding social activities.
Approach: They propose a framework that integrates emotion correlation learning, utilization, and supervising.
Outcome: The proposed framework improves empathetic perception and expression on a humanized dialogue dataset.
A Unified Framework for Emotion Identification and Generation in Dialogues (2023.eacl-srw)

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Challenge: Social chatbots have gained immense popularity and can be used to develop and promote social chatbot applications.
Approach: They propose a multi-task framework that jointly identifies the emotion of a given dialogue and generates response in accordance to the identified emotion.
Outcome: The proposed framework outperforms current state-of-the-art models with classification and generation loss.
Semi-Automatic Construction and Refinement of an Annotated Corpus for a Deep Learning Framework for Emotion Classification (2020.lrec-1)

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Challenge: Existing methods for emotion classification are expensive and require a large corpus of data.
Approach: They propose a method for creating a semi-automatically constructed emotion corpus by correcting errors in the corpus.
Outcome: The proposed method improves the quality of the emotion labels by correcting errors.
Modelling Context Emotions using Multi-task Learning for Emotion Controlled Dialog Generation (2021.eacl-main)

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Challenge: Recent research has tackled this task using neural generative methods by augmenting emotion classes with the input sequences.
Approach: They propose to use a self-attention based encoder and a decoder with dot product attention mechanism to generate a viable response with a specified emotion.
Outcome: The proposed model outperforms baselines on automatic evaluation measures such as F1 and BLEU scores, thus resulting in more fluent and adequate responses.

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