Syntax-aware data augmentation for neural machine translation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AWKKPAJAI" target="_blank" >RIV/00216208:11320/23:WKKPAJAI - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/abstract/document/10202198/" target="_blank" >https://ieeexplore.ieee.org/abstract/document/10202198/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TASLP.2023.3301214" target="_blank" >10.1109/TASLP.2023.3301214</a>

Result language

angličtina

Original language name

Syntax-aware data augmentation for neural machine translation

Original language description

"Data augmentation is an effective method for the performance enhancement of neural machine translation (NMT) by generating additional bilingual data. In this article, we propose a novel data augmentation strategy for neural machine translation. Unlike existing data augmentation methods that simply modify words with the same probability across different sentences, we introduce a sentence-specific probability approach for word selection based on the syntactic roles of words in the sentence. Our motivation is to consider a linguistics-motivated method to obtain more ingenious language generation rather than relying on computation-motivated approaches only. We argue that high-quality aligned bilingual data is crucial for NMT, and only computation-motivated data augmentation is insufficient to provide good enough extra enhancement data. Our approach leverages dependency parse trees of input sentences to determine the selection probability of each word in the sentence using three different functions to calculate probabilities for words with different depths. Besides, our method also revises the probability for words considering the sentence length. We evaluate our methods on multiple translation tasks. The experimental results demonstrate that our proposed data augmentation method does effectively boost existing sentence-independent methods for significant improvement of performance on translation tasks. Furthermore, an ablation study shows that our method does select fewer essential words and preserves the syntactic structure."

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

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

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Publication year

2023

Confidentiality

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

Name of the periodical

"IEEE/ACM Transactions on Audio, Speech, and Language Processing"

ISSN

2329-9290

e-ISSN

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Volume of the periodical

""

Issue of the periodical within the volume

2023-3-16

Country of publishing house

US - UNITED STATES

Number of pages

12

Pages from-to

2988 - 2999

UT code for WoS article

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EID of the result in the Scopus database

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