Papers by Maryam Ali
PolyWER: A Holistic Evaluation Framework for Code-Switched Speech Recognition (2024.findings-emnlp)
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| Challenge: | Existing methods for measuring accuracy, such as Word Error Rate (WER), are too strict to address this challenge. |
| Approach: | They propose a framework for evaluating speech recognition systems to handle language-mixing by appending annotations to a publicly available Arabic-English code-switched dataset. |
| Outcome: | The proposed framework evaluates speech recognition systems against human judgement and a publicly available Arabic-English code-switched dataset. |
“What’s Up, Doc?”: Analyzing How Users Seek Health Information in Large-Scale Conversational AI Datasets (2025.findings-emnlp)
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Akshay Paruchuri, Maryam Aziz, Rohit Vartak, Ayman Ali, Best Uchehara, Xin Liu, Ishan Chatterjee, Monica Agrawal
| Challenge: | a growing number of people are seeking healthcare information from large language models via chatbots, yet the nature and inherent risks of these interactions remain unexplored. |
| Approach: | They use a curated dataset of 11K real-world conversations composed of 25K user messages to analyze user interactions across 21 health specialties. |
| Outcome: | The proposed dataset consists of 11K real-world conversations composed of 25K user messages. |