Can Language Models Make Fun? A Case Study In Chinese Comical Crosstalk
Wang Benyou, Wu Xiangbo, Liu Xiaokang, Li Jianquan, Tiwari Prayag, Xie Qianqian. Arxiv 2022
[Paper]
[Code]
Language is the principal tool for human communication, in which humor is one
of the most attractive parts. Producing natural language like humans using
computers, a.k.a, Natural Language Generation (NLG), has been widely used for
dialogue systems, chatbots, machine translation, as well as computer-aid
creation e.g., idea generations, scriptwriting. However, the humor aspect of
natural language is relatively under-investigated, especially in the age of
pre-trained language models. In this work, we aim to preliminarily test whether
NLG can generate humor as humans do. We build a new dataset consisting of
numerous digitized Chinese Comical Crosstalk scripts (called C\(^3\) in short),
which is for a popular Chinese performing art called Xiangsheng' since 1800s.
(For convenience for non-Chinese speakers, we called crosstalk’ for
`Xiangsheng’ in this paper.) We benchmark various generation approaches
including training-from-scratch Seq2seq, fine-tuned middle-scale PLMs, and
large-scale PLMs (with and without fine-tuning). Moreover, we also conduct a
human assessment, showing that 1) large-scale pretraining largely improves
crosstalk generation quality; and 2) even the scripts generated from the best
PLM is far from what we expect, with only 65% quality of human-created
crosstalk. We conclude, humor generation could be largely improved using
large-scaled PLMs, but it is still in its infancy.
The data and benchmarking code is publicly available in
\url{https://github.com/anonNo2/crosstalk-generation}.
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