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Found 6 papers, 3 papers with code
Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias in Emotion Cause Pair Extraction
Besides, a clause graph is also established to model coarse-grained semantic relations between clauses.
Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations
To address this, we propose a supervised adversarial contrastive learning (SACL) framework for learning class-spread structured representations in a supervised manner.
Ranked #5 on
Emotion Recognition in Conversation
on EmoryNLP
Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension
Thus, model's comprehension ability towards real scenarios are hard to evaluate reasonably.
Natural Response Generation for Chinese Reading Comprehension
To this end, we construct a new dataset called Penguin to promote the research of MRC, providing a training and test bed for natural response generation to real scenarios.
Chinese Reading Comprehension
Machine Reading Comprehension
+1
Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation
Since the dependencies between speakers are complex and dynamic, which consist of intra- and inter-speaker dependencies, the modeling of speaker-specific information is a vital role in ERC.
Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias in Emotion-Cause Pair Extraction
Besides, a clause graph is also established to model coarse-grained semantic relations between clauses.
