CoMPM: Context Modeling with Speaker’s Pre-trained Memory Tracking for Emotion Recognition in Conversation (2022.naacl-main)
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| Challenge: | Emotion recognition in conversation is inaccurate if the previous utterances are not taken into account, so many studies reflect the dialogue context to improve the performance. |
| Approach: | They propose a method that combines pre-trained memory with the context model to improve the performance of the context models. |
| Outcome: | The proposed method achieves the first or second performance on all data and is state-of-the-art among systems that do not leverage structured data. |
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| Challenge: | Emotion Recognition in Conversation (ERC) aims to analyze the speaker’s emotional state in a conversation. |
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| Challenge: | Emotion recognition in conversation (ERC) is a task of discerning human emotions for each utterance within a conversation. |
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| Challenge: | Emotion recognition in conversation (ERC) is an advanced capability of conversational AI systems. |
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| Challenge: | Recent studies on ERC lack the ability to extract and integrate emotional clues from the conversational context. |
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Devamanyu Hazarika, Soujanya Poria, Amir Zadeh, Erik Cambria, Louis-Philippe Morency, Roger Zimmermann
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FERNet: Fine-grained Extraction and Reasoning Network for Emotion Recognition in Dialogues (2020.aacl-main)
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| Challenge: | Existing methods for emotion recognition in dialogues do not consider the content of the target utterance. |
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An Iterative Emotion Interaction Network for Emotion Recognition in Conversations (2020.coling-main)
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| Challenge: | Emotion recognition in conversations (ERC) is a task that aims to recognize the emotion of each utterance in conversations. |
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| Challenge: | Transformer-based language models (LMs) track contextual information through large, hard-coded input windows. |
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