Quantifying the Evaluation of Heuristic Methods for Textual Data Augmentation
By Omid Kashefi, Rebecca Hwa. 2020
15 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Information Systems, Natural Language Processing, Evaluation & Benchmarks
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Citations in the corpus
- A Survey on Data Augmentation for Text Classification (2021, methodology): “EDA is very popular in the research field and was used as a method for comparisons in the works of Qiu et al. [38], Huong and Hoang [39], Anaby-Tavor et al. [40], Kumar et al. [41], Bayer et al. [44], Feng et al. [35], Luu et al. [42], and Kashefi and Hwa [43].”