  Despite recent advances, standard sequence labeling systems often fail when
processing noisy user-generated text or consuming the output of an Optical
Character Recognition (OCR) process. In this paper, we improve the noise-aware
training method by proposing an empirical error generation approach that
employs a sequence-to-sequence model trained to perform translation from
error-free to erroneous text. Using an OCR engine, we generated a large
parallel text corpus for training and produced several real-world noisy
sequence labeling benchmarks for evaluation. Moreover, to overcome the data
sparsity problem that exacerbates in the case of imperfect textual input, we
learned noisy language model-based embeddings. Our approach outperformed the
baseline noise generation and error correction techniques on the erroneous
sequence labeling data sets. To facilitate future research on robustness, we
make our code, embeddings, and data conversion scripts publicly available.
