  While GPT has become the de-facto method for text generation tasks, its
application to pinyin input method remains unexplored. In this work, we make
the first exploration to leverage Chinese GPT for pinyin input method. We find
that a frozen GPT achieves state-of-the-art performance on perfect pinyin.
However, the performance drops dramatically when the input includes abbreviated
pinyin. A reason is that an abbreviated pinyin can be mapped to many perfect
pinyin, which links to even larger number of Chinese characters. We mitigate
this issue with two strategies, including enriching the context with pinyin and
optimizing the training process to help distinguish homophones. To further
facilitate the evaluation of pinyin input method, we create a dataset
consisting of 270K instances from 15 domains. Results show that our approach
improves performance on abbreviated pinyin across all domains. Model analysis
demonstrates that both strategies contribute to the performance boost.
