Papers with multi-class classification tasks
Your Pretrained Model Tells the Difficulty Itself: A Self-Adaptive Curriculum Learning Paradigm for Natural Language Understanding (2025.acl-srw)
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| Challenge: | Existing curriculum learning approaches rely on manually defined difficulty metrics which may not accurately reflect the model’s own perspective. |
| Approach: | They propose a self-adaptive curriculum learning paradigm that prioritizes fine-tuning examples based on difficulty scores predicted by pre-trained language models (PLMs) they evaluate four datasets covering binary and multi-class classification tasks. |
| Outcome: | The proposed model leads to faster convergence and improved performance compared to standard random sampling. |
Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks (2020.emnlp-main)
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| Challenge: | Existing methods for supervised meta-learning require many training tasks to generalize . cloze-style objectives can be used to generate a large, rich, meta-training task distribution from unlabeled text. |
| Approach: | They propose a self-supervised approach to generate a large, rich, meta-learning task distribution from unlabeled text. |
| Outcome: | The proposed approach generates a large, rich, meta-learning task distribution from unlabeled text. |
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. |
| Outcome: | The proposed models predict bragging with macro F1 up to 72.42 and 35.95 in binary and multi-class classification tasks respectively. |
SciPrompt: Knowledge-augmented Prompting for Fine-grained Categorization of Scientific Topics (2024.emnlp-main)
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| Challenge: | Recent studies have used prompt-based fine-tuning methods for text classification tasks . however, the difficulty and costs of manually selecting domain label terms for the verbalizer remain unexplored . |
| Approach: | They propose a framework to automatically retrieve scientific topic-related terms for low-resource text classification tasks. |
| Outcome: | The proposed method outperforms state-of-the-art methods on scientific text classification tasks under few and zero-shot settings. |
Shallow Domain Adaptive Embeddings for Sentiment Analysis (D19-1)
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| Challenge: | Existing domain adaptation algorithms for text classification are limited by lack of training data and exploiting domain idiosyncrasies to improve performance. |
| Approach: | They propose a domain adaptation layer that learns weights to combine a generic and a specific word embedding into a DA embeddable. |
| Outcome: | The proposed approach improves on binary and multi-class classification tasks using popular encoder architectures. |
Entailment as Robust Self-Learner (2023.acl-long)
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| Challenge: | Recent studies have found that entailment pretraining benefits weakly supervised fine-tuning. |
| Approach: | They propose a prompting strategy that formulates different NLU tasks as contextual entailment and propose an algorithm for better pseudo-labeling quality in self-training. |
| Outcome: | The proposed approach improves the zero-shot adaptation performance on downstream tasks. |