A fully automated and scalable Parallel Data Augmentation for Low Resource Languages using Image and Text Analytics
Linguistic diversity across the world creates a disparity with the availability of good quality digital language resources thereby restricting the technological benefits to majority of human population. The lack or absence of data resources makes it difficult to perform NLP tasks for low-resource languages. This paper presents a novel scalable and fully automated methodology to extract bilingual parallel corpora from newspaper articles using image and text analytics. We validate our approach by building parallel data corpus for two different language combinations and demonstrate the value of this dataset through a downstream task of machine translation and improve over the current baseline by close to 3 BLEU points.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationData AugmentationSimilar Papers 제목 키워드 기반
Adaptive Data Augmentation with Deep Parallel Generative Models
Data augmentation(DA) is a useful technique to enlarge the size of the training set and prevent overfitting for different machine learning tasks when training data is scarce. However, current data augmentation techniques…
BIG-bench Machine LearningData Augmentationimage-classificationImage Classification+1PAGANDA: An Adaptive Task-Independent Automatic Data Augmentation
Data augmentation is a useful technique to enlarge the size of the training set and prevent overfitting for different machine learning tasks when training data is scarce. However, current data augmentation techniques rel…
BIG-bench Machine LearningData Augmentationimage-classificationImage Classification+1Automated Snippet-Alignment Data Augmentation for Code Translation
Code translation aims to translate the code from its source language to the target language and is used in various software development scenarios. Recent developments in Large Language Models (LLMs) have showcased their …
Data AugmentationCode TranslationParallel Contextual Bandits in Wireless Handover Optimization
As cellular networks become denser, a scalable and dynamic tuning of wireless base station parameters can only be achieved through automated optimization. Although the contextual bandit framework arises as a natural cand…
Multi-Armed BanditsThompson SamplingScalable Teacher Forcing Network for Semi-Supervised Large Scale Data Streams
The large-scale data stream problem refers to high-speed information flow which cannot be processed in scalable manner under a traditional computing platform. This problem also imposes expensive labelling cost making the…
Data AugmentationDistributed ComputingModel Compression