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Found 7 papers, 4 papers with code
Adaptive Unsupervised Self-training for Disfluency Detection
Supervised methods have achieved remarkable results in disfluency detection.
Eval-GCSC: A New Metric for Evaluating ChatGPT's Performance in Chinese Spelling Correction
However, in the Chinese Spelling Correction (CSC) task, we observe a discrepancy: while ChatGPT performs well under human evaluation, it scores poorly according to traditional metrics.
Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection
Most existing approaches to disfluency detection heavily rely on human-annotated corpora, which is expensive to obtain in practice.
Combining ResNet and Transformer for Chinese Grammatical Error Diagnosis
Grammatical error diagnosis is an important task in natural language processing.
Multi-Task Self-Supervised Learning for Disfluency Detection
The pre-trained network is then fine-tuned using human-annotated disfluency detection training data.
Transition-Based Disfluency Detection using LSTMs
In this paper, we model the problem of disfluency detection using a transition-based framework, which incrementally constructs and labels the disfluency chunk of input sentences using a new transition system without syntax information.
A Neural Attention Model for Disfluency Detection
We treat disfluency detection as a sequence-to-sequence problem and propose a neural attention-based model which can efficiently model the long-range dependencies between words and make the resulting sentence more likely to be grammatically correct.
