Using Adversarial Examples in Natural Language Processing
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10390133" target="_blank" >RIV/00216208:11320/18:10390133 - isvavai.cz</a>
Result on the web
<a href="http://www.lrec-conf.org/proceedings/lrec2018/summaries/852.html" target="_blank" >http://www.lrec-conf.org/proceedings/lrec2018/summaries/852.html</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Using Adversarial Examples in Natural Language Processing
Original language description
Machine learning models have been providing promising results in many fields including natural language processing. These models are, nevertheless, prone to adversarial examples. These are artificially constructed examples which evince two main features: they resemble the real training data but they deceive already trained model. This paper investigates the effect of using adversarial examples during the training of recurrent neural networks whose text input is in the form of a sequence of word/character embeddings. The effects are studied on a compilation of eight NLP datasets whose interface was unified for quick experimenting. Based on the experiments and the dataset characteristics, we conclude that using the adversarial examples for NLP tasks that are modeled by recurrent neural networks provides a regularization effect and enables the training of models with greater number of parameters without overfitting. In addition, we discuss which combinations of datasets and model settings might benefit f
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<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2018
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 the 11th International Conference on Language Resources and Evaluation (LREC 2018)
ISBN
979-10-95546-00-9
ISSN
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e-ISSN
neuvedeno
Number of pages
8
Pages from-to
3693-3700
Publisher name
European Language Resources Association
Place of publication
Paris, France
Event location
Miyazaki, Japan
Event date
May 7, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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