Automatic Quality Estimation for Natural Language Generation: Ranting (Jointly Rating and Ranking)
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10405564" target="_blank" >RIV/00216208:11320/19:10405564 - isvavai.cz</a>
Result on the web
<a href="https://www.aclweb.org/anthology/W19-8644/" target="_blank" >https://www.aclweb.org/anthology/W19-8644/</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Automatic Quality Estimation for Natural Language Generation: Ranting (Jointly Rating and Ranking)
Original language description
We present a recurrent neural network based system for automatic quality estimation of natural language generation (NLG) outputs, which jointly learns to assign numerical ratings to individual outputs and to provide pairwise rankings of two different outputs. The latter is trained using pairwise hinge loss over scores from two copies of the rating network. We use learning to rank and synthetic data to improve the quality of ratings assigned by our system: we synthesise training pairs of distorted system outputs and train the system to rank the less distorted one higher. This leads to a 12% increase in correlation with human ratings over the previous benchmark. We also establish the state of the art on the dataset of relative rankings from the E2E NLG Challenge (Dušek et al., 2019), where synthetic data lead to a 4% accuracy increase over the base model.
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
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
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 the 12th International Conference on Natural Language Generation (INLG 2019)
ISBN
978-1-950737-94-9
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
369-376
Publisher name
Association for Computational Linguistics
Place of publication
Stroudsubrgh, PA, USA
Event location
Tokyo, Japan
Event date
Oct 29, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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