  Robust language processing systems are becoming increasingly important given
the recent awareness of dangerous situations where brittle machine learning
models can be easily broken with the presence of noises. In this paper, we
introduce a robust word recognition framework that captures multi-level
sequential dependencies in noised sentences. The proposed framework employs a
sequence-to-sequence model over characters of each word, whose output is given
to a word-level bi-directional recurrent neural network. We conduct extensive
experiments to verify the effectiveness of the framework. The results show that
the proposed framework outperforms state-of-the-art methods by a large margin
and they also suggest that character-level dependencies can play an important
role in word recognition.
