How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context

How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context

Qian Liu, Bei Chen, Jiaqi Guo, Jian-Guang Lou, Bin Zhou, Dongmei Zhang

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 3580-3586. https://doi.org/10.24963/ijcai.2020/495

Recently semantic parsing in context has received a 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.
Keywords:
Natural Language Processing: Natural Language Semantics
Natural Language Processing: Coreference Resolution
Natural Language Processing: Dialogue
Natural Language Processing: Natural Language Processing