  Recently semantic parsing in context has received considerable attention,
which is challenging since there are complex contextual phenomena. Previous
works verified their proposed methods in limited scenarios, which motivates us
to conduct an exploratory study on context modeling methods under real-world
semantic parsing in context. We present a grammar-based decoding semantic
parser and adapt typical context modeling methods on top of it. We evaluate 13
context modeling methods on two large complex cross-domain datasets, and our
best model achieves state-of-the-art performances on both datasets with
significant improvements. Furthermore, we summarize the most frequent
contextual phenomena, with a fine-grained analysis on representative models,
which may shed light on potential research directions. Our code is available at
https://github.com/microsoft/ContextualSP.
