Papers with mixup
SMSMix: Sense-Maintained Sentence Mixup for Word Sense Disambiguation (2022.findings-emnlp)
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| Challenge: | Word Sense Disambiguation (WSD) is an NLP task aimed at determining the correct sense of a word in a sentence from discrete sense choices. |
| Approach: | They propose a word-level mixup method that maintains the sense of a target word by increasing the frequency of least frequent senses during training to reduce the distributional bias of senses. |
| Outcome: | The proposed method shows high accuracy on the most frequent sense (MFS) and low accuracy on least frequent senses (LFS). |
CIAug: Equipping Interpolative Augmentation with Curriculum Learning (2022.naacl-main)
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| Challenge: | Current methods for interpolative data augmentation select samples at random, which might make it difficult for the model to generalize better and converge faster. |
| Approach: | They propose a curriculum-based learning method that leverages the relative position of samples in hyperbolic embedding space as a complexity measure to gradually mix up increasingly difficult and diverse samples along training. |
| Outcome: | The proposed method achieves state-of-the-art results over existing methods on 10 benchmark datasets across 4 languages in text classification and named-entity recognition tasks. |
Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity Recognition (2022.findings-acl)
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| Challenge: | Named Entity Recognition (NER) systems perform well on in-distribution data, but perform poorly on examples drawn from a shifted distribution. |
| Approach: | They propose to use expert-guided heuristics to change entity tokens and their contexts to alter their entity types as adversarial attacks. |
| Outcome: | The proposed model significantly improves performance on the challenging set and out-of-domain generalization. |
SSMix: Saliency-Based Span Mixup for Text Classification (2021.findings-acl)
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| Challenge: | SSMix synthesizes a sentence while preserving the locality of two original texts by span-based mixing and keeping more tokens related to the prediction relying on saliency information. |
| Approach: | They propose a new method where the operation is performed on input text rather than on hidden vectors like previous approaches. |
| Outcome: | The proposed method outperforms hidden-level mixup methods on a wide range of text classification benchmarks including textual entailment, sentiment classification, and questiontype classification. |
Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting (2022.naacl-main)
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| Challenge: | Prior research has focused on reducing noise for specific methods to achieve an effective integration. |
| Approach: | They propose to use token substitution and mixup to improve named entity recognition (NER) using a meta-reweighting strategy, which is extensible and requires little effort. |
| Outcome: | The proposed method is extensible, imposing little effort on a specific self-augmentation method. |
Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks (2020.coling-main)
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| Challenge: | Recent work on data augmentation techniques that interpolate inputs and labels shows strong effectiveness in image classification. |
| Approach: | They propose to integrate mixup to transformer-based pre-trained architecture for NLP tasks while keeping the whole end-to-end training system. |
| Outcome: | The proposed framework improves on GLUEbenchmark and transformer-based learning models while keeping the whole end-to-end training system. |
Dynamic Nonlinear Mixup with Distance-based Sample Selection (2022.coling-1)
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| Challenge: | Existing methods to augment data with mixup are limited by the space of synthetic data and its regularization effect. |
| Approach: | They propose a dynamic nonlinear mixup with distance-based sample selection which generates multiple sample pairs based on the distance between each sample. |
| Outcome: | The proposed method outperforms state-of-the-art methods on multiple public datasets. |
On the Calibration of Pre-trained Language Models using Mixup Guided by Area Under the Margin and Saliency (2022.acl-long)
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| Challenge: | Existing studies have shown that mixing up can improve model calibration on image classification tasks, but little is known about using it on natural language understanding (NLU) tasks. |
| Approach: | They propose a mixup strategy for pre-trained language models that improves model calibration further by using the AUM statistic and saliency map. |
| Outcome: | The proposed mixup improves model calibration on natural language understanding tasks while maintaining competitive accuracy. |
Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks (2023.emnlp-main)
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Haoqi Zheng, Qihuang Zhong, Liang Ding, Zhiliang Tian, Xin Niu, Changjian Wang, Dongsheng Li, Dacheng Tao
| Challenge: | Text classification tasks often encounter few-shot scenarios with limited labeled data, and addressing data scarcity is crucial. |
| Approach: | They propose a self-evolution learning (SE) based mixup approach for data augmentation in text classification which generates more adaptive and model-friendly pseudo samples for the model training. |
| Outcome: | The proposed approach can generate more adaptive and model-friendly pseudo samples for the model training. |
SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup (2020.emnlp-main)
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| Challenge: | Existing active sequence labeling methods use the queried samples alone in each iteration, which is inefficient for leveraging human annotations. |
| Approach: | They propose a data augmentation method to augment queried samples by generating extra labeled sequences in each iteration. |
| Outcome: | The proposed method improves the standard active sequence labeling method by 2.27%–3.75% in terms of F1 scores. |
Adapters Mixup: Mixing Parameter-Efficient Adapters to Enhance the Adversarial Robustness of Fine-tuned Pre-trained Text Classifiers (2024.emnlp-main)
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| Challenge: | Existing studies show that augmenting the training data of pre-trained language models with parametric fine-tuning methods can enhance their robustness under adversarial attacks. |
| Approach: | They propose an approach that fine-tunes PLMs with adapters and adversarial augmentation via mixup to leverage existing knowledge from a set of pre-known attacks. |
| Outcome: | The proposed approach achieves best trade-off between training efficiency and robustness under adversarial attacks compared to baselines on five downstream tasks across six varied black-box attacks and 2 PLMs. |