@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."
}
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<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.</abstract>
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%0 Journal Article
%T Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing
%A Cao, Junjie
%A Lin, Zi
%A Sun, Weiwei
%A Wan, Xiaojun
%J Computational Linguistics
%D 2021
%8 March
%V 47
%N 1
%I MIT Press
%C Cambridge, MA
%F cao-etal-2021-comparing
%X 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.
%R 10.1162/coli_a_00395
%U https://aclanthology.org/2021.cl-1.3/
%U https://doi.org/10.1162/coli_a_00395
%P 43-68
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