@inproceedings{hershcovich-etal-2020-comparison,
title = "Comparison by Conversion: Reverse-Engineering {UCCA} from Syntax and Lexical Semantics",
author = "Hershcovich, Daniel and
Schneider, Nathan and
Dvir, Dotan and
Prange, Jakob and
de Lhoneux, Miryam and
Abend, Omri",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.264",
doi = "10.18653/v1/2020.coling-main.264",
pages = "2947--2966",
abstract = "Building robust natural language understanding systems will require a clear characterization of whether and how various linguistic meaning representations complement each other. To perform a systematic comparative analysis, we evaluate the mapping between meaning representations from different frameworks using two complementary methods: (i) a rule-based converter, and (ii) a supervised delexicalized parser that parses to one framework using only information from the other as features. We apply these methods to convert the STREUSLE corpus (with syntactic and lexical semantic annotations) to UCCA (a graph-structured full-sentence meaning representation). Both methods yield surprisingly accurate target representations, close to fully supervised UCCA parser quality{---}indicating that UCCA annotations are partially redundant with STREUSLE annotations. Despite this substantial convergence between frameworks, we find several important areas of divergence.",
}
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<abstract>Building robust natural language understanding systems will require a clear characterization of whether and how various linguistic meaning representations complement each other. To perform a systematic comparative analysis, we evaluate the mapping between meaning representations from different frameworks using two complementary methods: (i) a rule-based converter, and (ii) a supervised delexicalized parser that parses to one framework using only information from the other as features. We apply these methods to convert the STREUSLE corpus (with syntactic and lexical semantic annotations) to UCCA (a graph-structured full-sentence meaning representation). Both methods yield surprisingly accurate target representations, close to fully supervised UCCA parser quality—indicating that UCCA annotations are partially redundant with STREUSLE annotations. Despite this substantial convergence between frameworks, we find several important areas of divergence.</abstract>
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%0 Conference Proceedings
%T Comparison by Conversion: Reverse-Engineering UCCA from Syntax and Lexical Semantics
%A Hershcovich, Daniel
%A Schneider, Nathan
%A Dvir, Dotan
%A Prange, Jakob
%A de Lhoneux, Miryam
%A Abend, Omri
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F hershcovich-etal-2020-comparison
%X Building robust natural language understanding systems will require a clear characterization of whether and how various linguistic meaning representations complement each other. To perform a systematic comparative analysis, we evaluate the mapping between meaning representations from different frameworks using two complementary methods: (i) a rule-based converter, and (ii) a supervised delexicalized parser that parses to one framework using only information from the other as features. We apply these methods to convert the STREUSLE corpus (with syntactic and lexical semantic annotations) to UCCA (a graph-structured full-sentence meaning representation). Both methods yield surprisingly accurate target representations, close to fully supervised UCCA parser quality—indicating that UCCA annotations are partially redundant with STREUSLE annotations. Despite this substantial convergence between frameworks, we find several important areas of divergence.
%R 10.18653/v1/2020.coling-main.264
%U https://aclanthology.org/2020.coling-main.264
%U https://doi.org/10.18653/v1/2020.coling-main.264
%P 2947-2966
Markdown (Informal)
[Comparison by Conversion: Reverse-Engineering UCCA from Syntax and Lexical Semantics](https://aclanthology.org/2020.coling-main.264) (Hershcovich et al., COLING 2020)
ACL