Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing

Junjie Cao, Zi Lin, Weiwei Sun, Xiaojun Wan


Abstract
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.
Anthology ID:
2021.cl-1.3
Volume:
Computational Linguistics, Volume 47, Issue 1 - March 2021
Month:
March
Year:
2021
Address:
Cambridge, MA
Venue:
CL
SIG:
Publisher:
MIT Press
Note:
Pages:
43–68
Language:
URL:
https://aclanthology.org/2021.cl-1.3
DOI:
10.1162/coli_a_00395
Bibkey:
Cite (ACL):
Junjie Cao, Zi Lin, Weiwei Sun, and Xiaojun Wan. 2021. Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing. Computational Linguistics, 47(1):43–68.
Cite (Informal):
Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing (Cao et al., CL 2021)
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PDF:
https://preview.aclanthology.org/update-css-js/2021.cl-1.3.pdf