Papers by Awais Athar
PronouncUR: An Urdu Pronunciation Lexicon Generator (L18-1)
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| Challenge: | acoustic modeling, large text data and a pronunciation lexicon are the bottlenecks for speech recognition systems for resource scarce languages. |
| Approach: | They propose a grapheme-to-phoneme conversion tool that generates a pronunciation lexicon from a list of Urdu words. |
| Outcome: | The proposed tool predicts 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. |
Generalists vs. Specialists: Evaluating Large Language Models for Urdu (2024.findings-emnlp)
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| Challenge: | Urdu is underrepresented in natural language processing, yet it is underserved. |
| Approach: | They compare general-purpose models with special-purpose ones that have been fine-tuned on specific tasks. |
| Outcome: | The proposed models outperform general-purpose models on seven classification and seven generation tasks. |
WER We Stand: Benchmarking Urdu ASR Models (2025.coling-main)
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| Challenge: | This paper analyzes the performance of three ASR models for low-resource languages like Urdu . low-rural languages like urdu have significant gaps in accuracy and reliability . |
| Approach: | They evaluate the performance of three ASR models: Whisper, MMS, and Seamless-M4T . they present the first conversational speech dataset for benchmarking Urdu ASR systems . |
| Outcome: | The proposed model families outperform Whisper, MMS, and Seamless-M4T on two types of speech datasets. |
SimplifyUR: Unsupervised Lexical Text Simplification for Urdu (2020.lrec-1)
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| Challenge: | Existing methods for text simplification for Urdu rely on manual lexicons and simplified corpora, but are not applicable to the language. |
| Approach: | They propose an unsupervised method for automatic text simplification for Urdu using word embeddings and morphological features. |
| Outcome: | The proposed method achieves BLEU score of 80.15 and SARI score of 42.02 on simple text generated on simplified corpora and human evaluations for correctness, grammaticality, meaning-preservation and simplicity. |
Kahaani: A Multimodal Co-Creative Storytelling System (2026.eacl-srw)
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| Challenge: | Kahaani is a multimodal, co-creative storytelling system that leverages Generative Artificial Intelligence to address the challenge of sustaining engagement to foster educational narrative experiences. |
| Approach: | They propose a multimodal, co-creative storytelling system that leverages Generative Artificial Intelligence to help children develop their storytelling skills. |
| Outcome: | The proposed system combines large language models, text-to-speech, and music generation to produce a rich, immersive, and accessible storytelling experience. |
Urdu Word Segmentation using Conditional Random Fields (CRFs) (C18-1)
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| Challenge: | Urdu is amongst languages for which word segmentation is a complex task as it exhibits space omission and space insertion issues. |
| Approach: | They propose a word segmentation system for Urdu which uses a Conditional Random Field sequence modeler with orthographic, linguistic and morphological features. |
| Outcome: | The proposed system achieves an F1 score for word boundary identification and 0.85 for sub-word boundary identification tasks. |
UQA: Corpus for Urdu Question Answering (2024.lrec-main)
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| Challenge: | Urdu is a low-resource language with over 70 million native speakers . expanding the reach of NLP to languages other than English is crucial for advancing multilingual AI systems. |
| Approach: | They introduce a novel dataset for question answering and text comprehension in Urdu . they use a technique called EATS which preserves the answer spans in translated context paragraphs . |
| Outcome: | The proposed dataset preserves answer spans in translated context paragraphs. |