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Low-Resource Machine Translation Training Curriculum Fit for Low-Resource Languages
We conduct an empirical study of neural machine translation (NMT) for truly low-resource languages, and present a training curriculum fit for cases...
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Navigating Linguistic Diversity: In-Context Learning and Prompt Engineering for Subjectivity Analysis in Low-Resource Languages
This research delves into subjectivity analysis through the lens of in-context learning and prompt engineering in low-resource languages, with a...
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Low-Resource Indic Languages Translation Using Multilingual Approaches
Machine translation is effective in the presence of a substantial parallel corpus. In a multilingual country like India, with diverse linguistic...
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Development of a Statistical Model for Automated Ground Truth Generation in Low-Resource Languages
We presents a computational model that can develop a ground truth for audio classification for any new and poorly resourced language. Here, we used...
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Multidimensional Affective Analysis for Low-Resource Languages: A Use Case with Guarani-Spanish Code-Switching Language
This paper focuses on text-based affective computing for Jopara, a code-switching language that combines Guarani and Spanish. First, we collected a...
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Fake News Detection in Low-Resource Languages
Fake news spreads much faster than real news. False information and misleading texts are the most important elements that lead to disasters and even...
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Speech and Language Technologies for Low-Resource Languages First International Conference, SPELLL 2022, Kalavakkam, India, November 23–25, 2022, Proceedings
This book constitutes refereed proceedings from the First International Conference on Speech and Language Technologies for Low-resource Languages,...
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Exploiting bilingual lexicons to improve multilingual embedding-based document and sentence alignment for low-resource languages
Neural machine translation systems trained on low-resource languages produce sub-optimal results due to the scarcity of large parallel datasets. To...
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Speech and Language Technologies for Low-Resource Languages Second International Conference, SPELLL 2023, Perundurai, Erode, India, December 6–8, 2023, Revised Selected Papers
This book constitutes the refereed conference proceedings of the second International Conference on Speech and Language Technologies for Low-Resource...
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Sign Language Recognition for Low Resource Languages Using Few Shot Learning
Sign Language Recognition (SLR) with machine learning is challenging due to the scarcity of data for most low-resource sign languages. Therefore, it...
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Comparing Transformer-Based Machine Translation Models for Low-Resource Languages of Colombia and Mexico
This paper offers a comparative analysis of two state-of-the-art machine translation models for Spanish to Indigenous languages of Colombia and...
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Future Directions in Defence NLP: Investigating Research Gaps for Low-Resource Languages
This paper examines the integration of Natural Language Processing (NLP) technologies within security and defence domains, particularly focusing on...
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Exploring ASR Models in Low-Resource Languages: Use-Case the Macedonian Language
We explore the use of Wav2Vec 2.0, NeMo, and ESPNet models trained on a dataset in Macedonian language for the development of Automatic Speech...
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Semantic-aware entity alignment for low resource language knowledge graph
Entity alignment (EA) is an important technique aiming to find the same real entity between two different source knowledge graphs (KGs). Current...
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Extremely Low-resource Multilingual Neural Machine Translation for Indic Mizo Language
Machine translation requires a vast amount of parallel data in order to generate high-quality translations. Since many Indian languages lack...
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A Deep Learning Emotion Classification Framework for Low Resource Languages
Emotion classification from text is the process of identifying and classifying emotions expressed in textual data. Emotions can be feelings such as...
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Comparative study of low resource Digaru language using SMT and NMT
In recent years, the field of natural language processing has witnessed the widespread adoption of Neural Machine Translation (NMT) as a...
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A Cross-Document Coreference Resolution Approach to Low-Resource Languages
Coreference resolution is an important area of research in natural language processing that deals with the task of identifying and grou** all the...
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Enhancing Low-Resource Languages Question Answering with Syntactic Graph
Multilingual pre-trained language models (PLMs) facilitate zero-shot cross-lingual transfer from rich-resource languages to low-resource languages in...
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Breaking language barriers with ChatGPT: enhancing low-resource machine translation between algerian arabic and MSA
Neural machine translation (NMT) has become an essential tool for breaking down language barriers and facilitating communication between different...
