Abstract
This paper addresses challenges of Natural
Language Processing (NLP) on non-canonical
multilingual data in which two or more languages are mixed. It refers to code-switching
which has become more popular in our
daily life and therefore obtains an increasing
amount of attention from the research community. We report our experience that covers not only core NLP tasks such as normalisation, language identification, language modelling, part-of-speech tagging and dependency
parsing but also more downstream ones such
as machine translation and automatic speech
recognition. We highlight and discuss the key
problems for each of the tasks with supporting
examples from different language pairs and
relevant previous work.
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