Milind Agarwal


PALI: A Language Identification Benchmark for Perso-Arabic Scripts
Sina Ahmadi | Milind Agarwal | Antonios Anastasopoulos
Tenth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2023)

The Perso-Arabic scripts are a family of scripts that are widely adopted and used by various linguistic communities around the globe. Identifying various languages using such scripts is crucial to language technologies and challenging in low-resource setups. As such, this paper sheds light on the challenges of detecting languages using Perso-Arabic scripts, especially in bilingual communities where “unconventional” writing is practiced. To address this, we use a set of supervised techniques to classify sentences into their languages. Building on these, we also propose a hierarchical model that targets clusters of languages that are more often confused by the classifiers. Our experiment results indicate the effectiveness of our solutions.


Collecting Verified COVID-19 Question Answer Pairs
Adam Poliak | Max Fleming | Cash Costello | Kenton Murray | Mahsa Yarmohammadi | Shivani Pandya | Darius Irani | Milind Agarwal | Udit Sharma | Shuo Sun | Nicola Ivanov | Lingxi Shang | Kaushik Srinivasan | Seolhwa Lee | Xu Han | Smisha Agarwal | João Sedoc
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

We release a dataset of over 2,100 COVID19 related Frequently asked Question-Answer pairs scraped from over 40 trusted websites. We include an additional 24, 000 questions pulled from online sources that have been aligned by experts with existing answered questions from our dataset. This paper describes our efforts in collecting the dataset and summarizes the resulting data. Our dataset is automatically updated daily and available at scraping-qas. So far, this data has been used to develop a chatbot providing users information about COVID-19. We encourage others to build analytics and tools upon this dataset as well.