@inproceedings{opitz-etal-2023-amr4nli,
title = "{AMR}4{NLI}: Interpretable and robust {NLI} measures from semantic graphs",
author = "Opitz, Juri and
Wein, Shira and
Steen, Julius and
Frank, Anette and
Schneider, Nathan",
editor = "Amblard, Maxime and
Breitholtz, Ellen",
booktitle = "Proceedings of the 15th International Conference on Computational Semantics",
month = jun,
year = "2023",
address = "Nancy, France",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.iwcs-1.29",
pages = "275--283",
abstract = "The task of natural language inference (NLI) asks whether a given premise (expressed in NL) entails a given NL hypothesis. NLI benchmarks contain human ratings of entailment, but the meaning relationships driving these ratings are not formalized. Can the underlying sentence pair relationships be made more explicit in an interpretable yet robust fashion? We compare semantic structures to represent premise and hypothesis, including sets of *contextualized embeddings* and *semantic graphs* (Abstract Meaning Representations), and measure whether the hypothesis is a semantic substructure of the premise, utilizing interpretable metrics. Our evaluation on three English benchmarks finds value in both contextualized embeddings and semantic graphs; moreover, they provide complementary signals, and can be leveraged together in a hybrid model.",
}
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<abstract>The task of natural language inference (NLI) asks whether a given premise (expressed in NL) entails a given NL hypothesis. NLI benchmarks contain human ratings of entailment, but the meaning relationships driving these ratings are not formalized. Can the underlying sentence pair relationships be made more explicit in an interpretable yet robust fashion? We compare semantic structures to represent premise and hypothesis, including sets of *contextualized embeddings* and *semantic graphs* (Abstract Meaning Representations), and measure whether the hypothesis is a semantic substructure of the premise, utilizing interpretable metrics. Our evaluation on three English benchmarks finds value in both contextualized embeddings and semantic graphs; moreover, they provide complementary signals, and can be leveraged together in a hybrid model.</abstract>
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%0 Conference Proceedings
%T AMR4NLI: Interpretable and robust NLI measures from semantic graphs
%A Opitz, Juri
%A Wein, Shira
%A Steen, Julius
%A Frank, Anette
%A Schneider, Nathan
%Y Amblard, Maxime
%Y Breitholtz, Ellen
%S Proceedings of the 15th International Conference on Computational Semantics
%D 2023
%8 June
%I Association for Computational Linguistics
%C Nancy, France
%F opitz-etal-2023-amr4nli
%X The task of natural language inference (NLI) asks whether a given premise (expressed in NL) entails a given NL hypothesis. NLI benchmarks contain human ratings of entailment, but the meaning relationships driving these ratings are not formalized. Can the underlying sentence pair relationships be made more explicit in an interpretable yet robust fashion? We compare semantic structures to represent premise and hypothesis, including sets of *contextualized embeddings* and *semantic graphs* (Abstract Meaning Representations), and measure whether the hypothesis is a semantic substructure of the premise, utilizing interpretable metrics. Our evaluation on three English benchmarks finds value in both contextualized embeddings and semantic graphs; moreover, they provide complementary signals, and can be leveraged together in a hybrid model.
%U https://aclanthology.org/2023.iwcs-1.29
%P 275-283
Markdown (Informal)
[AMR4NLI: Interpretable and robust NLI measures from semantic graphs](https://aclanthology.org/2023.iwcs-1.29) (Opitz et al., IWCS 2023)
ACL