Exploiting Open IE for Deriving Multiple Premises Entailment Corpus
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F19%3A00110584" target="_blank" >RIV/00216224:14330/19:00110584 - isvavai.cz</a>
Result on the web
<a href="http://lml.bas.bg/ranlp2019/proceedings-ranlp-2019.pdf" target="_blank" >http://lml.bas.bg/ranlp2019/proceedings-ranlp-2019.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.26615/978-954-452-056-4_144" target="_blank" >10.26615/978-954-452-056-4_144</a>
Alternative languages
Result language
angličtina
Original language name
Exploiting Open IE for Deriving Multiple Premises Entailment Corpus
Original language description
Natural language inference (NLI) is a key part of natural language understanding. The NLI task is defined as a decision problem whether a given sentence -- hypothesis -- can be inferred from a given text. Typically, we deal with a text consisting of just a single premise/single sentence, which is called a single premise entailment (SPE) task. Recently, a derived task of NLI from multiple premises (MPE) was introduced together with the first annotated corpus and corresponding several strong baselines. Nevertheless, the further development in MPE field requires accessibility of huge amounts of annotated data. In this paper we introduce a novel method for rapid deriving of MPE corpora from an existing NLI (SPE) annotated data that does not require any additional annotation work. This proposed approach is based on using an open information extraction system. We demonstrate the application of the method on a well known SNLI corpus. Over the obtained corpus, we provide the first evaluations as well as we state a strong baseline.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of Recent Advances in Natural Language Processing
ISBN
9789544520557
ISSN
2603-2813
e-ISSN
1313-8502
Number of pages
8
Pages from-to
1257-1264
Publisher name
2019
Place of publication
Varna
Event location
Varna
Event date
Jan 1, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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