Code-switching patterns can be an effective route to improve performance of downstream NLP applications: A case study of humour, sarcasm and hate speech detection
This work addresses the challenge of detecting nuanced language phenomena like humour and hate speech for NLP practitioners, but it appears incremental as it applies a known linguistic observation to specific tasks.
The paper tackled the problem of improving humour, sarcasm, and hate speech detection in NLP by utilizing code-switching patterns, resulting in enhanced performance for these downstream applications.
In this paper we demonstrate how code-switching patterns can be utilised to improve various downstream NLP applications. In particular, we encode different switching features to improve humour, sarcasm and hate speech detection tasks. We believe that this simple linguistic observation can also be potentially helpful in improving other similar NLP applications.