| Challenge: | Detecting disfluency can be difficult because of the flexible nature of reparandum structure and the lack of a nested structure. |
| Approach: | They propose a semi-supervised approach which extracts hidden features from self-attention without any Recurrent Neural Network (RNN) or Convolutional Neural Net (CNN). |
| Outcome: | The proposed approach improves over baselines by using unlabelled data . identifying and removing non-fluent factors would help to improve spontaneous speech quality . |
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| Challenge: | Recent studies on disfluency detection heavily relies on human annotations, which are difficult and expensive to obtain in practice. |
| Approach: | They propose an unsupervised method that reweights the importance of each training example according to its grammatical feature and prediction confidence. |
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Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection (2020.emnlp-main)
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| Challenge: | Existing approaches to disfluency detection rely on human annotations, which are expensive to obtain. |
| Approach: | They propose an unsupervised learning paradigm which can work with unlabeled text corpora. |
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Image Captioning with Very Scarce Supervised Data: Adversarial Semi-Supervised Learning Approach (D19-1)
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| Challenge: | Recent work on image captioning has made impressive progress . however, the results are limited and the model is difficult to train . |
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Improving Disfluency Detection by Self-Training a Self-Attentive Model (2020.acl-main)
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| Challenge: | Existing self-attentive parsers using contextualized word embeddings produce state-of-the-art results in joint parsing and disfluency detection. |
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Self-Training with Weak Supervision (2021.naacl-main)
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| Challenge: | State-of-the-art deep neural networks require large amounts of labeled training data that is expensive to obtain or not available for many tasks. |
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Disfluency Detection using Auto-Correlational Neural Networks (D18-1)
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| Challenge: | a recent study proposes an auto-correlational neural network (ACNN) that can detect disfluency in speech . the model uses a convolutional neural system and augments it with a new auto-corrector . |
| Approach: | They propose a convolutional neural network model that captures "rough copy" dependencies . the model is based on a new auto-correlation operator that capture the kinds of "rough copies" dependency . |
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GAN Driven Semi-distant Supervision for Relation Extraction (N19-1)
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| Challenge: | Existing methods for relation extraction are limited to costly hand-labeled training sets and hard to be extended to large-scale relations. |
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Neural Networks Against (and For) Self-Training: Classification with Small Labeled and Large Unlabeled Sets (2023.findings-acl)
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| Challenge: | Existing models for text classification suffer from the semantic drift problem, which is a problem for self-training. |
| Approach: | They propose a semi-supervised text classifier based on self-training using one positive and one negative property of neural networks. |
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Semi-supervised Relation Extraction via Incremental Meta Self-Training (2021.findings-emnlp)
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| Challenge: | Existing methods suffer from the gradual drift problem, where noisy pseudo labels are incorporated during training. |
| Approach: | They propose a method that uses pseudo labels to assess quality on unlabeled samples . they use a relation label generation network to learn from successful and failed attempts . |
| Outcome: | Experimental results show the proposed method can improve on two public datasets. |
Semi-Supervised Learning for Video Captioning (2020.findings-emnlp)
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| Challenge: | Existing video captioning algorithms are heavily dependent on supervised training data. |
| Approach: | They propose to train the video captioning model on labeled and unlabeled data jointly in a semi-supervised learning manner. |
| Outcome: | The proposed model outperforms state-of-the-art semi-supervised learning approaches on VATEX, MSR-VTT and MSVD datasets. |