In this chapter, we have examined an approach to NLP competitions, specifically Google Quest Q&A Labeling. We began with a baseline utilizing vintage methods (summary/descriptive characteristics of the text fields), combined with embeddings from a pretrained model. This gave us a foundational understanding of the challenges involved, and we then moved on to a discussion of more advanced solutions that performed well in the competition. This chapter should give you an understanding of how to approach NLP classification contests; those new to the field will benefit from the baseline solution, while more experienced Kagglers can benefit from the guidance that the published medal approaches provide.
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