OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00351131" target="_blank" >RIV/68407700:21230/21:00351131 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.18653/v1/2021.naacl-industry.30" target="_blank" >https://doi.org/10.18653/v1/2021.naacl-industry.30</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/2021.naacl-industry.30" target="_blank" >10.18653/v1/2021.naacl-industry.30</a>

Result language

angličtina

Original language name

OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation

Original language description

Detecting an Out-of-Domain (OOD) utterance is crucial for a robust dialog system. Most dialog systems are trained on a pool of annotated OOD data to achieve this goal. However, collecting the annotated OOD data for a given domain is an expensive process. To mitigate this issue, previous works have proposed generative adversarial networks (GAN) based models to generate OOD data for a given domain automatically. However, these proposed models do not work directly with the text. They work with the text’s latent space instead, enforcing these models to include components responsible for encoding text into latent space and decoding it back, such as auto-encoder. These components increase the model complexity, making it difficult to train. We propose OodGAN, a sequential generative adversarial network (SeqGAN) based model for OOD data generation. Our proposed model works directly on the text and hence eliminates the need to include an auto-encoder. OOD data generated using OodGAN model outperforms state-of-the-art in OOD detection metrics for ROSTD (67% relative improvement in FPR 0.95) and OSQ datasets (28% relative improvement in FPR 0.95) (Zheng et al., 2020).

Czech name

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Czech description

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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)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers

ISBN

978-1-954085-47-3

ISSN

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e-ISSN

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Number of pages

8

Pages from-to

238-245

Publisher name

Association for Computational Linguistics (ACL)

Place of publication

Stroudsburg

Event location

Online

Event date

Jun 6, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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