Gözde Gül Şahin; To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP. Computational Linguistics 2022; 48 (1): 5–42. doi: https://doi.org/10.1162/coli_a_00425
Data-hungry deep neural networks have established themselves as the de facto standard for many NLP tasks, including the traditional sequence tagging ones. Despite their state-of-the-art performance on high-resource languages, they still fall behind their statistical counterparts in low-resource scenarios. One methodology to counterattack this problem is text augmentation, that is, generating new synthetic training data points from existing data. Although NLP has recently witnessed several new textual augmentation techniques, the field still lacks a systematic performance analysis on a diverse set of languages and sequence tagging tasks. To fill this gap, we investigate three categories of text augmentation methodologies that perform changes on the syntax (e.g., cropping sub-sentences), token (e.g., random word insertion), and character (e.g., character swapping) levels. We systematically compare the methods on part-of-speech tagging, dependency parsing, and semantic role labeling for a diverse set of language families using various models, including the architectures that rely on pretrained multilingual contextualized language models such as mBERT . Augmentation most significantly improves dependency parsing, followed by part-of-speech tagging and semantic role labeling. We find the experimented techniques to be effective on morphologically rich languages in general rather than analytic languages such as Vietnamese. Our results suggest that the augmentation techniques can further improve over strong baselines based on mBERT , especially for dependency parsing. We identify the character-level methods as the most consistent performers, while synonym replacement and syntactic augmenters provide inconsistent improvements. Finally, we discuss that the results most heavily depend on the task, language pair (e.g., syntactic-level techniques mostly benefit higher-level tasks and morphologically richer languages), and model type (e.g., token-level augmentation provides significant improvements for BPE , while character-level ones give generally higher scores for char and mBERT based models).
Recent advancements in the natural language processing (NLP) field have led to models that surpass all previous results on a range of high-level downstream applications, such as machine translation, text classification, dependency parsing, and many more. However, these models require a huge number of training data points to achieve state-of-the-art scores and are known to suffer from the out-of-domain problem. In other words, they are not able to correctly label or generate novel, unseen data points. In order to boost the performance of such systems in the presence of low data, the researchers have introduced various data augmentation techniques that aim to increase the sample size and also the variation of the lexical (Wei and Zou 2019; Fadaee, Bisazza, and Monz 2017; Kobayashi 2018; Karpukhin et al. 2019) or syntactic patterns (Vickrey and Koller 2008; Şahin and Steedman 2018; Gulordava et al. 2018). A similar line of research introduced adversarial attack and defense mechanisms (Belinkov and Bisk 2018; Karpukhin et al. 2019) based on injecting noises with the goal of more robust NLP systems.