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Found 6 papers, 2 papers with code
UQA: Corpus for Urdu Question Answering
This paper introduces UQA, a novel dataset for question answering and text comprehension in Urdu, a low-resource language with over 70 million native speakers.
To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation
A weighted hybrid score that combines uncertainty and diversity is then used to select the top instances for annotation in each AL iteration.
Representative Subset Selection for Efficient Fine-Tuning in Self-Supervised Speech Recognition
Self-supervised speech recognition models require considerable labeled training data for learning high-fidelity representations for Automatic Speech Recognition (ASR) which is computationally demanding and time-consuming.
SimplifyUR: Unsupervised Lexical Text Simplification for Urdu
Being a low-resource language in terms of standard linguistic resources, recent text simplification approaches that rely on manually crafted simplified corpora or lexicons such as WordNet are not applicable to Urdu.
Urdu Word Segmentation using Conditional Random Fields (CRFs)
State-of-the-art Natural Language Processing algorithms rely heavily on efficient word segmentation.
PronouncUR: An Urdu Pronunciation Lexicon Generator
The tool predicts the pronunciation of words using a LSTM-based model trained on a handcrafted expert lexicon of around 39, 000 words and shows an accuracy of 64% upon internal evaluation.
