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Found 6 papers, 5 papers with code
Efficient Cross-Task Prompt Tuning for Few-Shot Conversational Emotion Recognition
Emotion Recognition in Conversation (ERC) has been widely studied due to its importance in developing emotion-aware empathetic machines.
Computational Efficiency
Emotion Recognition in Conversation
One2Set: Generating Diverse Keyphrases as a Set
In this work, we propose a new training paradigm One2Set without predefining an order to concatenate the keyphrases.
Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement Learning
In response to this problem, we propose a new fine-grained evaluation metric to improve the RL framework, which considers different granularities: token-level $F_1$ score, edit distance, duplication, and prediction quantities.
Pre-trained Models for Natural Language Processing: A Survey
Recently, the emergence of pre-trained models (PTMs) has brought natural language processing (NLP) to a new era.
Improving BERT Fine-Tuning via Self-Ensemble and Self-Distillation
Fine-tuning pre-trained language models like BERT has become an effective way in NLP and yields state-of-the-art results on many downstream tasks.
How to Fine-Tune BERT for Text Classification?
Language model pre-training has proven to be useful in learning universal language representations.
Ranked #1 on
Text Classification
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