  Data-hungry deep neural networks have established themselves as the 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 of their statistical counter-parts in low-resource scenarios. One
methodology to counter attack this problem is text augmentation, i.e.,
generating new synthetic training data points from existing data. Although NLP
has recently witnessed a load of 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 which 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 them 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. 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, and the model type.
