Papers with semi-supervised approaches
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding (2021.findings-emnlp)
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| Challenge: | Task-adaptive pre-training (TAPT) and Self-training can be complementary with simple TFS protocol. |
| Approach: | They propose to use task-adaptive pre-training and self-training to combine TAPT and ST with a simple TFS protocol to achieve strong combined gains across six datasets. |
| Outcome: | The proposed method can achieve strong combined gains across six datasets covering sentiment classification, paraphrase identification, natural language inference, named entity recognition and dialogue slot classification. |
Programmable Annotation with Diversed Heuristics and Data Denoising (2022.coling-1)
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| Challenge: | Neural natural language generation and understanding models require massive amounts of annotated data to be competitive. |
| Approach: | They propose a data programming framework that can jointly construct labeled data for language generation and understanding tasks by allowing annotators to modify an automatically-inferred alignment rule set between sequence labels and text. |
| Outcome: | The proposed framework generates high-quality data within a 1.48 BLEU and 6.42 slot F1 of 100% human-labeled data with just 100 labeled data samples outperforming benchmark annotation frameworks and other semi-supervised approaches. |
Semi-supervised New Event Type Induction and Description via Contrastive Loss-Enforced Batch Attention (2023.eacl-main)
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| Challenge: | Existing methods for event extraction use annotated event types but are expensive and time-consuming. |
| Approach: | They propose a semi-supervised approach to learning new event types using a masked contrastive loss. |
| Outcome: | The proposed method learns similarities between clusters by enforcing an attention mechanism over the data minibatch. |
Detecting Urgency Status of Crisis Tweets: A Transfer Learning Approach for Low Resource Languages (2020.coling-main)
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| Challenge: | We train monolingual and cross-lingual classifiers on the extracted features of tweets . we use a few state-of-the-art contextual embeddings to extract features of the tweets. |
| Approach: | They propose to use tweets to train a dataset of English and two low-resource languages to train zero-shot transfer models. |
| Outcome: | The proposed model performs well in English and in low-resource languages . the proposed model is based on state-of-the-art embeddings and semi-supervised methods . |
Noise-injected Consistency Training and Entropy-constrained Pseudo Labeling for Semi-supervised Extractive Summarization (2022.coling-1)
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| Challenge: | Existing studies on semi-supervised learning methods focus on how to effectively utilize abundant unlabeled data. |
| Approach: | They propose a semi-supervised consistency training method to regularize model predictions and a pseudo-labeling strategy to obtain high-confidence labels from unlabeled predictions. |
| Outcome: | The proposed method improves extractive summarization over an insufficient labeled dataset. |
Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation (2023.acl-long)
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| Challenge: | Named Entity Recognition and Relation Extraction are two crucial tasks in Information Extraction. |
| Approach: | They propose a framework for joint semi-supervised entity and relation extraction that captures the global structure information between tasks and exploits interactions within unlabeled data. |
| Outcome: | The proposed framework outperforms state-of-the-art semi-supervised approaches on NER and RE tasks. |
Leveraging Information Redundancy of Real-World Data through Distant Supervision (2024.lrec-main)
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| Challenge: | Existing methods for annotation of health care notes are promising but they are limited due to privacy regulations. |
| Approach: | They propose a text labeling method that leverages the redundancy of temporal information in a data lake to create a large programmatically annotated corpus and train transformer models using distant supervision. |
| Outcome: | The proposed method reduces expert annotation time, a scarce and expensive resource. |