Results 11 to 20 of about 972,067 (310)
The Zeno’s Paradox of ‘Low-Resource’ Languages [PDF]
Proceedings of the 2024 Conference on Empirical Methods in Natural Language ProcessingThe disparity in the languages commonly studied in Natural Language Processing (NLP) is typically reflected by referring to languages as low vs high-resourced. However, there is limited consensus on what exactly qualifies as a ‘low-resource language.’ To
H. Nigatu +4 more
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Multilingual Offensive Language Identification for Low-resource Languages [PDF]
ACM Transactions on Asian and Low-Resource Language Information Processing, 2021 Offensive content is pervasive in social media and a reason for concern to companies and government organizations. Several studies have been recently published investigating methods to detect the various forms of such content (e.g., hate speech, cyberbullying, and cyberaggression). The clear majority of these studies deal with English partially because
Tharindu Ranasinghe, Marcos Zampieri
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Voice Activation for Low-Resource Languages
Applied Sciences, 2021 Voice activation systems are used to find a pre-defined word or phrase in the audio stream. Industry solutions, such as “OK, Google” for Android devices, are trained with millions of samples. In this work, we propose and investigate several ways to train
Aliaksei Kolesau, Dmitrij Šešok
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Toward Zero-Shot and Zero-Resource Multilingual Question Answering
IEEE Access, 2022 In recent years, multilingual question answering has been an emergent research topic and has attracted much attention. Although systems for English and other rich-resource languages that rely on various advanced deep learning-based techniques have been ...
Chia-Chih Kuo, Kuan-Yu Chen
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Neural Machine Translation for Low-resource Languages: A Survey [PDF]
ACM Computing Surveys, 2021 Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since the early 2000s and has already entered a mature phase. While considered the most widely used solution for Machine Translation, its performance on low-resource ...
Surangika Ranathunga +5 more
semanticscholar +1 more source
Neural network language models for low resource languages [PDF]
Interspeech 2014, 2014 For resource rich languages, recent works have shown Neural Network based Language Models (NNLMs) to be an effective modeling technique for Automatic Speech Recognition, out performing standard n-gram language models (LMs). For low resource languages, however, the performance of NNLMs has not been well explored.
Gandhe, Ankur, Metze, Florian, Lane, Ian
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Data augmentation for low resource languages [PDF]
Interspeech 2014, 2014 Recently there has been interest in the approaches for training speech recognition systems for languages with limited resources. Under the IARPA Babel program such resources have been provided for a range of languages to support this research area.
Ragni, Anton +3 more
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Endangered Languages are not Low-Resourced [PDF]
, 2021 The term low-resourced has been tossed around in the field of natural language processing to a degree that almost any language that is not English can be called "low-resourced"; sometimes even just for the sake of making a mundane or mediocre paper appear more interesting and insightful.
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Fake News Detection in Low Resource Languages using SetFit Framework
Inteligencia Artificial, 2023 Social media has become an integral part of people’s lives, resulting in a constant flow of information. However, a concerning trend has emerged with the rapid spread of fake news, attributed to the lack of verification mechanisms.
Amin Abdedaiem +2 more
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Constituency Parsing by Cross-Lingual Delexicalization
IEEE Access, 2021 Cross-lingual transfer is an important technique for low-resource language processing. Temporarily, most research on syntactic parsing works on the dependency structures. This work investigates cross-lingual parsing on another type of important syntactic
Hour Kaing +5 more
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