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. |
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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. |
Towards Emotion-aided Multi-modal Dialogue Act Classification (2020.acl-main)
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| Challenge: | Considerable work on Dialogue Act Classification (DAC) has been done on textual inputs. |
| Approach: | They propose to use a multimodal Emotion aware Dialogue Act dataset to explore the role of multi-modality and emotion recognition in DAC. |
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In Search of the Lost Arch in Dialogue: A Dependency Dialogue Acts Corpus for Multi-Party Dialogues (2025.findings-acl)
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Jon Cai, Brendan King, Peyton Cameron, Susan Windisch Brown, Miriam Eckert, Dananjay Srinivas, George Arthur Baker, V Kate Everson, Martha Palmer, James Martin, Jeffrey Flanigan
| Challenge: | Understanding speaker intentions remains a challenge in NLP . a number of corpora annotated using theoretical frameworks of dialogue focus on utterance-level labeling of speaker intent, missing wider context, or the rhetorical structure of a dialogue. |
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EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems (2022.lrec-1)
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Shutong Feng, Nurul Lubis, Christian Geishauser, Hsien-chin Lin, Michael Heck, Carel van Niekerk, Milica Gasic
| Challenge: | Existing emotion-annotated task-oriented corpora are limited in size, label richness, and public availability, creating a bottleneck for downstream tasks. |
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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. |
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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 . |
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Sentence and Clause Level Emotion Annotation, Detection, and Classification in a Multi-Genre Corpus (L18-1)
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| Challenge: | Existing methods for predicting emotion categories are limited due to their multi-label nature . e.g. anger, joy, sadness are difficult to predict due to inherent multi-genre nature - a problem that is often overlooked in single-genrete text. |
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Dialogue Act Classification with Context-Aware Self-Attention (N19-1)
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| Challenge: | Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks. |
| Approach: | They propose a hierarchical deep neural network to model different levels of utterance and dialogue act semantics and use contextual dependencies to improve performance. |
| Outcome: | The proposed model improves on the Switchboard Dialogue Act Corpus while maintaining high accuracy. |
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation (D19-1)
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| Challenge: | Emotion recognition in conversation (ERC) has received much attention lately due to its potential widespread applications in diverse areas, such as health-care, education, and human resources. |
| Approach: | They propose a graph neural network-based approach to emotion recognition in conversation that leverages self and inter-speaker dependency of the interlocutors to model conversational context. |
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The ISO Standard for Dialogue Act Annotation, Second Edition (2020.lrec-1)
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Harry Bunt, Volha Petukhova, Emer Gilmartin, Catherine Pelachaud, Alex Fang, Simon Keizer, Laurent Prévot
| Challenge: | ISO standard 24617-2 for dialogue act annotation has been used in corpus annotation and in the design of components for spoken and multimodal interactive systems. |
| Approach: | ISO standard 24617-2 for dialogue act annotation is proposed for a second edition . this second edition allows a more accurate annotation of dependence relations and rhetorical relations in dialogue. |
| Outcome: | The proposed second edition of ISO 24617-2 for dialogue act annotation addresses some inaccuracies and undesirable limitations. |