Genre Transfer in NMT: Creating Synthetic Spoken Parallel Sentences using Written Parallel Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10492530" target="_blank" >RIV/00216208:11320/22:10492530 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11320/23:10475925
Result on the web
<a href="https://www.lcs2.in/ICON-2022/index.html" target="_blank" >https://www.lcs2.in/ICON-2022/index.html</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Genre Transfer in NMT: Creating Synthetic Spoken Parallel Sentences using Written Parallel Data
Original language description
Text style transfer (TST) aims to control attributes in a given text without changing the content. The matter gets complicated when the boundary separating two styles gets blurred. We can notice similar difficulties in the case of parallel datasets in spoken and written genres. Genuine spoken features like filler words and repetitions in the existing spoken genre parallel datasets are often cleaned during transcription and translation, making the texts closer to written datasets. This poses several problems for spoken genre-specific tasks like simultaneous speech translation. This paper seeks to address the challenge of improving spoken language translations. We start by creating a genre classifier for individual sentences and then try two approaches for data augmentation using written examples: (1) a novel method that involves assembling and disassembling spoken and written neural machine translation (NMT) models, and (2) a rule-based method to inject spoken features. Though the observed results for
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
<a href="/en/project/GX19-26934X" target="_blank" >GX19-26934X: Neural Representations in Multi-modal and Multi-lingual Modeling</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
19th International Conference on Natural Language Processing
ISBN
978-1-959429-38-8
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
224-233
Publisher name
Association for Computational Linguistics
Place of publication
Stroudsburg, PA, USA
Event location
Delhi, India
Event date
Dec 15, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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