EasyAug: An Automatic Textual Data Augmentation Platform for Classification Tasks

Qiu, Siyuan; Xu, Binxia; Zhang, Jie; Wang, Yafang; Shen, Xiaoyu; Melo, Gerard de; Long, Chong; Li, Xiaolong

doi:10.1145/3366424.3383552

Cited by 46 publications

(37 citation statements)

References 6 publications

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“…Adding additional knowledge is also popular in NLP models [10,28,49,51]. It can be applied as a kind of feature enrichment.…”

Section: Related Workmentioning

confidence: 99%

Learning Fine-grained Fact-Article Correspondence in Legal Cases

Ge¹,

huang²,

Shen³

et al. 2021

Preprint

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Automatically recommending relevant law articles to a given legal case has attracted much attention as it can greatly release human labor from searching over the large database of laws. However, current researches only support coarse-grained recommendation where all relevant articles are predicted as a whole without explaining which specific fact each article is relevant with. Since one case can be formed of many supporting facts, traversing over them to verify the correctness of recommendation results can be timeconsuming. We believe that learning fine-grained correspondence between each single fact and law articles is crucial for an accurate and trustworthy AI system. With this motivation, we perform a pioneering study and create a corpus with manually annotated factarticle correspondences. We treat the learning as a text matching task and propose a multi-level matching network to address it. To help the model better digest the content of law articles, we parse articles in form of premise-conclusion pairs with random forest. Experiments show that the parsed form yielded better performance and the resulting model surpassed other popular text matching baselines. Furthermore, we compare with previous researches and find that establishing the fine-grained fact-article correspondences can improve the recommendation accuracy by a large margin. Our best system reaches an F1 score of 96.3%, making it of great potential for practical use. It can also significantly boost the downstream

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“…Adding additional knowledge is also popular in NLP models [10,28,49,51]. It can be applied as a kind of feature enrichment.…”

Section: Related Workmentioning

confidence: 99%

Learning Fine-grained Fact-Article Correspondence in Legal Cases

Ge¹,

huang²,

Shen³

et al. 2021

Preprint

Self Cite

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“…], [? ], [30], [35], [36], rapid progress has been made by treating each sub-task in LJP as a text classification problem [37], [38]. Some works experimented conditioning a certain sub-task on other sub-tasks and showed improvement.…”

Section: Related Workmentioning

confidence: 99%

Dependency Learning for Legal Judgment Prediction with a Unified Text-to-Text Transformer

Yunyun¹,

Shen²,

Li³

et al. 2021

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Given the fact of a case, Legal Judgment Prediction (LJP) involves a series of sub-tasks such as predicting violated law articles, charges and term of penalty. We propose leveraging a unified text-to-text Transformer for LJP, where the dependencies among sub-tasks can be naturally established within the auto-regressive decoder. Compared with previous works, it has three advantages: (1) it fits in the pretraining pattern of masked language models, and thereby can benefit from the semantic prompts of each sub-task rather than treating them as atomic labels, (2) it utilizes a single unified architecture, enabling full parameter sharing across all sub-tasks, and (3) it can incorporate both classification and generative sub-tasks. We show that this unified transformer, albeit pretrained on general-domain text, outperforms pretrained models tailored specifically for the legal domain. Through an extensive set of experiments, we find that the best order to capture dependencies is different from human intuitions, and the most reasonable logical order for humans can be sub-optimal for the model. We further include two more auxiliary tasks: court view generation and article content prediction, showing they can not only improve the prediction accuracy, but also provide interpretable explanations for model outputs even when an error is made. With the best configuration, our model outperforms both previous SOTA and a single-tasked version of the unified transformer by a large margin. Code and dataset are available at https://github.com/oli-yun/Dependency-LJP.

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“…A straightforward approach to assess which heuristic and data augmentation approach is more appropriate for the task is to try every heuristic to generate an augmented dataset, then train a classifier on each and check the final classification performance (Qiu et al, 2020;Wei and Zou, 2019). The training process in this brute-force approach, however, may be time-consuming and resource-intensive, especially in complex training scenarios.…”

Section: Quantification Of Heuristics Suitabilitymentioning

confidence: 99%

Quantifying the Evaluation of Heuristic Methods for Textual Data Augmentation

Kashefi

Hwa

2020

Proceedings of the Sixth Workshop on Noisy User-Generated Text (W-Nut 2020)

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Data augmentation has been shown to be effective in providing more training data for machine learning and resulting in more robust classifiers. However, for some problems, there may be multiple augmentation heuristics, and the choices of which one to use may significantly impact the success of the training. In this work, we propose a metric for evaluating augmentation heuristics; specifically, we quantify the extent to which an example is "hard to distinguish" by considering the difference between the distribution of the augmented samples of different classes. Experimenting with multiple heuristics in two prediction tasks (positive/negative sentiment and verbosity/conciseness) validates our claims by revealing the connection between the distribution difference of different classes and the classification accuracy.

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EasyAug: An Automatic Textual Data Augmentation Platform for Classification Tasks

Cited by 46 publications

References 6 publications

Learning Fine-grained Fact-Article Correspondence in Legal Cases

Learning Fine-grained Fact-Article Correspondence in Legal Cases

Dependency Learning for Legal Judgment Prediction with a Unified Text-to-Text Transformer

Quantifying the Evaluation of Heuristic Methods for Textual Data Augmentation

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