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Main Authors: Kumari, Vibhuti, Kavi, Narayana Murthy
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2410.03194
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author Kumari, Vibhuti
Kavi, Narayana Murthy
author_facet Kumari, Vibhuti
Kavi, Narayana Murthy
contents In this paper we propose a novel method of augmenting parallel text corpora which promises good quality and is also capable of producing many fold larger corpora than the seed corpus we start with. We do not need any additional monolingual corpora. We use Multi-Lingual Masked Language Model to mask and predict alternative words in context and we use Sentence Embeddings to check and select sentence pairs which are likely to be translations of each other. We cross check our method using metrics for MT Quality Estimation. We believe this method can greatly alleviate the data scarcity problem for all language pairs for which a reasonable seed corpus is available.
format Preprint
id arxiv_https___arxiv_org_abs_2410_03194
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Parallel Corpus Augmentation using Masked Language Models
Kumari, Vibhuti
Kavi, Narayana Murthy
Computation and Language
In this paper we propose a novel method of augmenting parallel text corpora which promises good quality and is also capable of producing many fold larger corpora than the seed corpus we start with. We do not need any additional monolingual corpora. We use Multi-Lingual Masked Language Model to mask and predict alternative words in context and we use Sentence Embeddings to check and select sentence pairs which are likely to be translations of each other. We cross check our method using metrics for MT Quality Estimation. We believe this method can greatly alleviate the data scarcity problem for all language pairs for which a reasonable seed corpus is available.
title Parallel Corpus Augmentation using Masked Language Models
topic Computation and Language
url https://arxiv.org/abs/2410.03194