ALICE: Active Learning with Contrastive Natural Language Explanations (2020.emnlp-main)
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| Challenge: | Annotating a large dataset with annotations is costly and infeasible. |
| Approach: | They propose an expert-in-the-loop training framework that utilizes contrastive natural language explanations to improve data efficiency in learning. |
| Outcome: | The proposed framework outperforms baseline models trained with 40-100% more training data on bird species classification and social relationship classification tasks. |
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Label Anchored Contrastive Learning for Language Understanding (2022.naacl-main)
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| Challenge: | a novel approach to contrastive learning for language understanding is not fully explored . contrastive training has been widely applied to self-supervised representation learning . |
| Approach: | They propose a label anchored contrastive learning approach for language understanding using a class label. |
| Outcome: | The proposed approach improves on GLUE and CLUE benchmarks by 4.1% compared to the state-of-the-art approaches . the proposed approach also improves under the few-shot and data imbalance settings . |
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture (2023.findings-emnlp)
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Bingsheng Yao, Ishan Jindal, Lucian Popa, Yannis Katsis, Sayan Ghosh, Lihong He, Yuxuan Lu, Shashank Srivastava, Yunyao Li, James Hendler, Dakuo Wang
| Challenge: | Existing low-resource learning techniques focus on label annotation while neglecting the natural language explanation of a data point. |
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KFCNet: Knowledge Filtering and Contrastive Learning for Generative Commonsense Reasoning (2021.findings-emnlp)
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| Challenge: | Pre-trained language models have led to substantial gains over a broad range of NLP tasks, but have limitations for high-quality tasks such as commonsense generation and ad keyword generation. |
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Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning (2022.acl-long)
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| Challenge: | Pre-trained sequence-to-sequence language models generate structured outputs such as graphs with limited supervision. |
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Active Learning by Acquiring Contrastive Examples (2021.emnlp-main)
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| Challenge: | Using uncertainty and diversity sampling, active learning acquisition functions select difficult and diverse data points from a pool of unlabeled data. |
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| Outcome: | The proposed approach performs better or equal to the best performing baseline on all tasks, on both in-domain and out-of-domain data. |
An End-to-End Contrastive Self-Supervised Learning Framework for Language Understanding (2022.tacl-1)
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| Challenge: | Existing approaches to learning data representations using contrastive learning perform data augmentation and contrastive training separately. |
| Approach: | They propose a framework that performs data augmentation and contrastive learning end-to-end . they propose to combine data augmented with text encoders to optimize for contrastive training . |
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Contrastive Data and Learning for Natural Language Processing (2022.naacl-tutorials)
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| Challenge: | Current NLP models heavily rely on effective representation learning algorithms. |
| Approach: | This tutorial introduces contrastive learning and provides an introduction to the techniques. |
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Let the CAT out of the bag: Contrastive Attributed explanations for Text (2022.emnlp-main)
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| Challenge: | XAI has seen an explosion of interest in explaining black box behavior . contrastive/counterfactual explanations have seen a surge of interest recently . |
| Approach: | They propose a method which provides contrastive explanations for natural language text data with a novel twist by exploiting attribute classifiers. |
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Mitigating Dataset Artifacts in Natural Language Inference Through Automatic Contextual Data Augmentation and Learning Optimization (2022.lrec-1)
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| Challenge: | In recent years, natural language inference has been an emerging research area . a new data augmentation technique is used to augment pre-trained language models . |
| Approach: | They propose to combine automatic contextual data augmentation with a learning procedure for natural language inference. |
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KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference (2021.acl-long)
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| Challenge: | Existing approaches in NLP focus on “WHY A” rather than contrastive “WHA NOT B” Experimental results show that contrastive explanations are beneficial to fit the scenarios by clarifying the difference between the predicted answer and other possible wrong ones. |
| Approach: | They propose to generate contrastive explanations with counterfactual examples in NLI by identifying key phrases from input sentences and using them as key perturbations to generate counterfacts. |
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