  For many real-world applications, the user-generated inputs usually contain
various noises due to speech recognition errors caused by linguistic
variations1 or typographical errors (typos). Thus, it is crucial to test model
performance on data with realistic input noises to ensure robustness and
fairness. However, little study has been done to construct such benchmarks for
Chinese, where various language-specific input noises happen in the real world.
In order to fill this important gap, we construct READIN: a Chinese multi-task
benchmark with REalistic And Diverse Input Noises. READIN contains four diverse
tasks and requests annotators to re-enter the original test data with two
commonly used Chinese input methods: Pinyin input and speech input. We designed
our annotation pipeline to maximize diversity, for example by instructing the
annotators to use diverse input method editors (IMEs) for keyboard noises and
recruiting speakers from diverse dialectical groups for speech noises. We
experiment with a series of strong pretrained language models as well as robust
training methods, we find that these models often suffer significant
performance drops on READIN even with robustness methods like data
augmentation. As the first large-scale attempt in creating a benchmark with
noises geared towards user-generated inputs, we believe that READIN serves as
an important complement to existing Chinese NLP benchmarks. The source code and
dataset can be obtained from https://github.com/thunlp/READIN.
