Papers with Cross-View Training
Industry Scale Semi-Supervised Learning for Natural Language Understanding (2021.naacl-industry)
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| Challenge: | Obtaining human annotation is expensive and time-consuming process. |
| Approach: | They propose a semi-supervised learning pipeline which leverages millions of unlabeled examples to improve natural language understanding tasks. |
| Outcome: | The proposed pipeline can be used to improve natural language understanding tasks. |
Semi-Supervised Sequence Modeling with Cross-View Training (D18-1)
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| Challenge: | Unsupervised representation learning algorithms such as word2vec and ELMo only learn from task-specific labeled data during the main training phase. |
| Approach: | They propose a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data. |
| Outcome: | The proposed algorithm improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data. |
To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging (2020.emnlp-main)
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Kasturi Bhattacharjee, Miguel Ballesteros, Rishita Anubhai, Smaranda Muresan, Jie Ma, Faisal Ladhak, Yaser Al-Onaizan
| Challenge: | Using large amounts of unlabeled data to improve performance has become the foundation for many natural language processing tasks. |
| Approach: | They propose a task-specific semi-supervised approach that uses unlabeled data in a more task-agnostic manner. |
| Outcome: | The proposed approach achieves similar performance to BERT on a set of sequence tagging tasks with less financial and environmental impact. |