emotion-recognition-1
Emotion Recognition in Conversation
3260 papers • 126 benchmarks • 313 datasets
Given the transcript of a conversation along with speaker information of each constituent utterance, the ERC task aims to identify the emotion of each utterance from several pre-defined emotions. Formally, given the input sequence of N number of utterances [(u1, p1), (u2, p2), . . . , (uN , pN )], where each utterance ui = [ui,1, ui,2, . . . , ui,T ] consists of T words ui,j and spoken by party pi, the task is to predict the emotion label ei of each utterance ui. .
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Benchmarks
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IEMOCAP
IEMOCAP
MELD
MELD
DailyDialog
DailyDialog
Libraries
iUse these libraries to find emotion-recognition-1 models and implementations
6 papers 944
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Most implemented papers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Kenton Lee, Kristina Toutanova, Ming-Wei Chang•Mon Dec 31 2018
A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.
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