| Challenge: | Detecting disclosures of individuals’ employment status on social media is a challenging task due to their rarity in a sea of social media content and the variety of linguistic forms used to describe them. |
| Approach: | They propose to use BERT-based classification models to identify five types of disclosures about individuals’ employment status in three languages. |
| Outcome: | The proposed methods achieve significant gains in precision, recall, and diversity of results in real-world settings of extreme class imbalance. |
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Active Learning for BERT: An Empirical Study (2020.emnlp-main)
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Liat Ein-Dor, Alon Halfon, Ariel Gera, Eyal Shnarch, Lena Dankin, Leshem Choshen, Marina Danilevsky, Ranit Aharonov, Yoav Katz, Noam Slonim
| Challenge: | Existing approaches to deal with data scarcity are active learning (AL) and pre-trained models are not being considered. |
| Approach: | They propose to use active learning techniques to cope with data scarcity in binary text classification scenarios where the annotation budget is very small and the data is often skewed. |
| Outcome: | The proposed methods improve BERT performance in binary text classification scenarios where the annotation budget is very small and the data is often skewed. |
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks (2020.findings-emnlp)
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| Challenge: | Recent datasets heuristically choose examples to ensure label balance . state-of-the-art models trained on QQP and WikiQA have only 2.4% average precision . |
| Approach: | They show that recent datasets heuristically choose examples to ensure label balance . they instead use active learning to retrieve uncertain points from a large pool of unlabeled utterance pairs . |
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Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment (N19-3)
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| Challenge: | #MeToo movement provides platform to narrate personal experiences of sexual harassment. |
| Approach: | They propose a three-part ULMFiT architecture to tackle text subtleties in a classification task . they propose to annotate a manually annotated real-world dataset to test their approach . |
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A Semantics-based Approach to Disclosure Classification in User-Generated Online Content (2020.findings-emnlp)
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| Challenge: | Existing algorithms for self-disclosure identification and classification are challenging due to the relative anonymity of social networking sites and lack of non-verbal cues to signal thoughts or feelings. |
| Approach: | They propose an approach to detect emotional and informational self-disclosure in natural language by using frame semantics to identify lexical units and their semantic roles. |
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Cold-start Active Learning through Self-supervised Language Modeling (2020.emnlp-main)
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| Challenge: | Labeling data is a fundamental bottleneck in machine learning due to annotation cost and time. |
| Approach: | They propose a strategy that uses the pre-training loss to find examples that surprise the model and minimize labeling costs. |
| Outcome: | The proposed approach reduces labeling costs and costs by using pre-trained language models. |
Sensitive Data Detection and Classification in Spanish Clinical Text: Experiments with BERT (2020.lrec-1)
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| Challenge: | Massive digital data processing can endanger personal data privacy . anonymisation involves removing or replacing sensitive information from data . |
| Approach: | They propose to use a BERT-based sequence labelling model to conduct an experiment on clinical datasets in Spanish. |
| Outcome: | The proposed model outperforms existing models on clinical datasets in Spanish and shows that it is highly competitive with other models. |
Automatic Identification and Classification of Bragging in Social Media (2022.acl-long)
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| Challenge: | Bragging is a speech act employed to build a favorable self-image through positive statements about oneself. |
| Approach: | They propose to use tweets annotated for bragging to build a model that can predict bragging with macro F1 up to 72.42 and 35.95 for binary and multi-class bragging classification tasks respectively. |
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Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification (2022.lrec-1)
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| Challenge: | Large scale, multi-label text datasets with high numbers of different classes are expensive to annotate due to domain experts taking a lot of time working through all the classes. |
| Approach: | They propose to build classifiers on multi-label text datasets using Active Learning to reduce labeling effort. |
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A Checkpoint on Multilingual Misogyny Identification (2022.acl-srw)
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| Challenge: | a study on hate speech against minorities in Italian tweets found that 1 women are the most targeted group. |
| Approach: | They propose to train monolingual transformers and multilingual transformer models with monolingual data in English, Italian, and Spanish to detect misogyny in tweets. |
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Cross-Lingual Disaster-related Multi-label Tweet Classification with Manifold Mixup (2020.acl-srw)
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| Challenge: | Towards this goal, many studies have focused on disaster-related tweet classification. |
| Approach: | They compile a multilingual dataset for multi-label classification of disaster-related tweets . they show that their model generalizes to unseen disasters in the test set . |
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