Using Deep Learning to… Generate Puns? A fun application of state-of-the-art methods in natural language processing that demonstrates how far the field has come.
Natural language processing (NLP) as a field has seen unprecedented growth (especially in the past 2 years due to the publication of BERT). And while much research is focused on tasks with huge implications (e.g., question-answering, text summarization), we should always remember that there are some fun applications of natural language processing research as well. For example, generating puns.
Let’s delve into a recent paper by He et al. that proposes a pretty-strong model for pun generation. P.S. if you just want to see some of the puns that the model ended up generating, I’ve included the puns that the authors give at the end of the post.
The Dataset. First thing’s first. Like any deep learning problem, generating puns obviously requires a not-small dataset. Now, personally I wouldn’t have even thought that a dataset of puns exists. Regardless, the authors found a good collection of puns: the pun dataset from the 2017 SemEval task, which contains 1,099 different puns. Although this is on the small side of datasets, it’s enough to get an acceptably-performing model trained.
Yesterday I swallowed some food coloring. The doctor says I’m OK, but I feel like I dyed a little inside. (“dyed” vs “died”)
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