@article{cao-etal-2021-comparing,
title = "Comparing Knowledge-Intensive and Data-Intensive Models for {E}nglish Resource Semantic Parsing",
author = "Cao, Junjie and
Lin, Zi and
Sun, Weiwei and
Wan, Xiaojun",
journal = "Computational Linguistics",
volume = "47",
number = "1",
month = mar,
year = "2021",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/2021.cl-1.3",
doi = "10.1162/coli_a_00395",
pages = "43--68",
abstract = "In this work, we present a phenomenon-oriented comparative analysis of the two dominant approaches in English Resource Semantic (ERS) parsing: classic, knowledge-intensive and neural, data-intensive models. To reflect state-of-the-art neural NLP technologies, a factorization-based parser is introduced that can produce Elementary Dependency Structures much more accurately than previous data-driven parsers. We conduct a suite of tests for different linguistic phenomena to analyze the grammatical competence of different parsers, where we show that, despite comparable performance overall, knowledge- and data-intensive models produce different types of errors, in a way that can be explained by their theoretical properties. This analysis is beneficial to in-depth evaluation of several representative parsing techniques and leads to new directions for parser development.",
}
Markdown (Informal)
[Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing](https://aclanthology.org/2021.cl-1.3) (Cao et al., CL 2021)
ACL