@inproceedings{bernardy-chatzikyriakidis-2020-improving,
title = "Improving the Precision of Natural Textual Entailment Problem Datasets",
author = "Bernardy, Jean-Philippe and
Chatzikyriakidis, Stergios",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.844",
pages = "6835--6840",
abstract = "In this paper, we propose a method to modify natural textual entailment problem datasets so that they better reflect a more precise notion of entailment. We apply this method to a subset of the Recognizing Textual Entailment datasets. We thus obtain a new corpus of entailment problems, which has the following three characteristics: 1. it is precise (does not leave out implicit hypotheses) 2. it is based on {``}real-world{''} texts (i.e. most of the premises were written for purposes other than testing textual entailment). 3. its size is 150. Broadly, the method that we employ is to make any missing hypotheses explicit using a crowd of experts. We discuss the relevance of our method in improving existing NLI datasets to be more fit for precise reasoning and we argue that this corpus can be the basis a first step towards wide-coverage testing of precise natural-language inference systems.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>In this paper, we propose a method to modify natural textual entailment problem datasets so that they better reflect a more precise notion of entailment. We apply this method to a subset of the Recognizing Textual Entailment datasets. We thus obtain a new corpus of entailment problems, which has the following three characteristics: 1. it is precise (does not leave out implicit hypotheses) 2. it is based on “real-world” texts (i.e. most of the premises were written for purposes other than testing textual entailment). 3. its size is 150. Broadly, the method that we employ is to make any missing hypotheses explicit using a crowd of experts. We discuss the relevance of our method in improving existing NLI datasets to be more fit for precise reasoning and we argue that this corpus can be the basis a first step towards wide-coverage testing of precise natural-language inference systems.</abstract>
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%0 Conference Proceedings
%T Improving the Precision of Natural Textual Entailment Problem Datasets
%A Bernardy, Jean-Philippe
%A Chatzikyriakidis, Stergios
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F bernardy-chatzikyriakidis-2020-improving
%X In this paper, we propose a method to modify natural textual entailment problem datasets so that they better reflect a more precise notion of entailment. We apply this method to a subset of the Recognizing Textual Entailment datasets. We thus obtain a new corpus of entailment problems, which has the following three characteristics: 1. it is precise (does not leave out implicit hypotheses) 2. it is based on “real-world” texts (i.e. most of the premises were written for purposes other than testing textual entailment). 3. its size is 150. Broadly, the method that we employ is to make any missing hypotheses explicit using a crowd of experts. We discuss the relevance of our method in improving existing NLI datasets to be more fit for precise reasoning and we argue that this corpus can be the basis a first step towards wide-coverage testing of precise natural-language inference systems.
%U https://aclanthology.org/2020.lrec-1.844
%P 6835-6840
Markdown (Informal)
[Improving the Precision of Natural Textual Entailment Problem Datasets](https://aclanthology.org/2020.lrec-1.844) (Bernardy & Chatzikyriakidis, LREC 2020)
ACL