Papers with data augmentations
Low-resource neural machine translation with morphological modeling (2024.findings-naacl)
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| Challenge: | Existing methods for character-based and sub-word tokenization are limited to the surface forms of the words. |
| Approach: | They propose a framework-solution for modeling complex morphology in low-resource settings using a transformer architecture and beam search-based decoder. |
| Outcome: | The proposed model improves translation performance on Kinyarwanda English translation using public-domain parallel text. |
AugCSE: Contrastive Sentence Embedding with Diverse Augmentations (2022.aacl-main)
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| Challenge: | Similar work has shown that a single augmentation can be used to learn a robust generalpurpose representation with contrastive learning. |
| Approach: | They propose a unified framework to utilize diverse sets of data augmentations to achieve a better, general-purpose sentence embedding model. |
| Outcome: | The proposed framework achieves state-of-the-art results on downstream transfer tasks and performs competitively on semantic textual similarity tasks, using only unsupervised data. |
Bag of Tricks for In-Distribution Calibration of Pretrained Transformers (2023.findings-eacl)
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| Challenge: | Recent studies show that pre-trained language models (PLMs) often predict over-confidently. |
| Approach: | They propose to use ensemble learning and data augmentation to improve confidence calibration for PLMs by combining calibration techniques with a trade-off between accuracy and classification. |
| Outcome: | The proposed calibration method improves classification accuracy and confidence in pre-trained language models by combining several calibration techniques. |
Virtual Augmentation Supported Contrastive Learning of Sentence Representations (2022.findings-acl)
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| Challenge: | Despite profound successes, contrastive representation learning relies on carefully designed data augmentations using domain-specific knowledge. |
| Approach: | They propose a virtual augmentation supported Contrastive Learning of sentence representations . they approximate the neighborhood of an instance via its K-nearest in-batch neighbors . |
| Outcome: | The proposed model outperforms existing methods on a wide range of downstream tasks. |
Semi-supervised Relation Extraction via Data Augmentation and Consistency-training (2023.eacl-main)
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| Challenge: | Obtaining high-quality human labelled data is an expensive and noisy process. |
| Approach: | They propose to leverage unlabelled data to improve the sample efficiency of the models. |
| Outcome: | The proposed methods can be used to extract the Cause-Effect relation between a given head entity and tail entity based on context in the input sentence. |
Set-Aligning Framework for Auto-Regressive Event Temporal Graph Generation (2024.naacl-long)
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| Challenge: | Existing methods for constructing event temporal graphs have been suboptimal . authors propose a set-aligning framework for the effective utilisation of Large Language Models . |
| Approach: | They propose a set-aligning framework for the effective utilisation of Large Language Models to alleviate text generation loss penalties. |
| Outcome: | The proposed framework surpasses existing baselines for event temporal graph generation. |
Domain Confused Contrastive Learning for Unsupervised Domain Adaptation (2022.naacl-main)
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| Challenge: | Existing studies on domain-shifting adaptations have focused on domain . |
| Approach: | They propose a self-supervised approach to unsupervised domain adduction using domain puzzles to bridge the source and target domains and retain discriminative representations after adaptation. |
| Outcome: | The proposed approach outperforms baselines and further ablation studies show that it is more stable and effective when performing other data augmentations. |
Consistency Regularization for Cross-Lingual Fine-Tuning (2021.acl-long)
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Bo Zheng, Li Dong, Shaohan Huang, Wenhui Wang, Zewen Chi, Saksham Singhal, Wanxiang Che, Ting Liu, Xia Song, Furu Wei
| Challenge: | Experimental results show that consistency regularization improves cross-lingual fine-tuning . pre-trained cross-linguistic models can transfer task-specific supervision from one language to the other . |
| Approach: | They propose to improve cross-lingual fine-tuning with consistency regularization . they use example consistency regularized to penalize prediction sensitivity to four types of data augmentations . |
| Outcome: | The proposed method improves cross-lingual fine-tuning across tasks . it can be generalized to other target languages without additional training . |
Implicit Discourse Relation Classification: We Need to Talk about Evaluation (2020.acl-main)
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| Challenge: | Lack of consistency in preprocessing and evaluation poses challenges to fair comparison of results in literature. |
| Approach: | They propose an improved evaluation protocol for implicit relation classification on PDTB 2.0 . they report strong baseline results from pretrained sentence encoders . |
| Outcome: | The proposed evaluation protocol improves the existing framework and provides strong baseline results. |
DALE: Generative Data Augmentation for Low-Resource Legal NLP (2023.emnlp-main)
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Sreyan Ghosh, Chandra Kiran Reddy Evuru, Sonal Kumar, S Ramaneswaran, S Sakshi, Utkarsh Tyagi, Dinesh Manocha
| Challenge: | DALE addresses the challenges existing frameworks pose in generating effective data augmentations of legal documents. |
| Approach: | They propose a generative Data Augmentation framework for low-resource legal NLP that exploits domain-specific language characteristics of templated legal documents to mask collocated spans of text. |
| Outcome: | The proposed framework outperforms baseline frameworks on 13 datasets and 4 low-resource settings. |
Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training (2023.findings-acl)
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| Challenge: | Dense retrievers have impressive performance, but their demand for abundant training data limits their application scenarios. |
| Approach: | They propose a method which uses unlabeled data to construct pseudo-positive examples from unlabelled data and then contrastively weighs the contrastive loss of different pairs according to the estimated relevance. |
| Outcome: | The proposed method beats the SOTA unsupervised Contriever model on BEIR and open-domain QA retrieval benchmarks and is a good few-shot learner. |
To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering (2023.acl-long)
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| Challenge: | Recent advances in open-domain question answering have demonstrated impressive accuracy on general-purpose domains like Wikipedia. |
| Approach: | They propose a more realistic end-to-end domain shift evaluation setting covering five diverse domains to assess model adaption. |
| Outcome: | The proposed model improves by 24 points when adapted to unsupervised datasets. |