Challenges of Computational Processing of Code-Switching
It tackles processing difficulties for code-switching, which is increasingly common in daily communication, but is incremental as it synthesizes existing challenges without presenting new solutions.
This paper addresses challenges in Natural Language Processing for code-switched multilingual data, covering core tasks like normalization and language identification as well as downstream applications such as machine translation, while highlighting key problems with examples from various language pairs.
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 cov- ers 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.