Papers by Agha Ali Raza
Empathy Applicability Modeling for General Health Queries (2026.findings-acl)
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| Challenge: | Existing NLP frameworks focus on reactively labeling empathy in doctors’ responses but offer limited support for anticipatory modeling of empathy needs, especially in general health queries. |
| Approach: | They propose an Empathy Applicability Framework that classifies patient queries in terms of the applicability of emotional reactions and interpretations based on clinical, contextual, and linguistic cues. |
| Outcome: | The Empathy Applicability Framework outperforms heuristic and zero-shot LLMs in the clinical setting. |
Language Model-Driven Data Pruning Enables Efficient Active Learning (2026.findings-eacl)
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| Challenge: | Existing data pruning methods for active learning are expensive and time-consuming. |
| Approach: | They propose a plug-and-play data pruning strategy that leverages language models to prune the unlabeled pool. |
| Outcome: | The proposed pruning strategy outperforms existing pruning methods on translation, sentiment analysis, topic classification, and summarization tasks on diverse datasets. |
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. |
PakBBQ: A Culturally Adapted Bias Benchmark for QA (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are widely adopted in language processing applications, but they often perpetuate harmful societal biases. |
| Approach: | They propose a culturally and regionally adapted extension of the original Bias Benchmark for Question Answering dataset to address this gap. |
| Outcome: | The proposed model gains 12% accuracy with disambiguation and stronger counter bias behaviors in Urdu than in English. |
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. |
Deepfake Defense: Constructing and Evaluating a Specialized Urdu Deepfake Audio Dataset (2024.findings-acl)
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Sheza Munir, Wassay Sajjad, Mukeet Raza, Emaan Abbas, Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza
| Challenge: | Automatic speaker verification systems are facing escalating challenges due to deepfake attacks. |
| Approach: | They propose a Urdu deepfake audio dataset for deepfak detection focusing on two spoofing attacks – Tacotron and VITS TTS. |
| Outcome: | The proposed dataset evaluates two spoofing attacks in Urdu with a human evaluation to gauge whether people are able to distinguish deepfake audios from real (bonafide) audios. |
To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation (2025.coling-main)
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| Challenge: | Active learning (AL) techniques reduce labeling costs for training neural machine translation models by selecting smaller representative subsets from unlabeled data for annotation. |
| Approach: | They propose an AL strategy that combines uncertainty and diversity for sentence selection. |
| Outcome: | The proposed method prioritizes diverse instances having high model uncertainty for annotation in early iterations. |
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. |