India has a rich linguistic landscape with languages from 4 major language families spoken
by over a billion people. 22 of these languages are listed in the Constitution of India …

We present, Naamapadam, the largest publicly available Named Entity Recognition (NER)
dataset for the 11 major Indian languages from two language families. The dataset contains …

Natural Language Processing (NLP) has been in practice for the past couple of decades,
and extensive work has been done for the Western languages, particularly the English …

Despite growing efforts to halt distasteful content on social media, multilingualism has added
a new dimension to this problem. The scarcity of resources makes the challenge even …

Despite the considerable advancements in English LLMs, the progress in building
comparable models for other languages has been hindered due to the scarcity of tailored …

Deep learning based text-to-speech (TTS) systems have been evolving rapidly with
advances in model architectures, training methodologies, and generalization across …

We create publicly available language identification (LID) datasets and models in all 22
Indian languages listed in the Indian constitution in both native-script and romanized text …

Large multilingual pretrained language models (mPLMs) have become the de facto state of
the art for cross-lingual transfer in NLP. However, their large-scale deployment to many …

While most transliteration research is focused on single tokens such as named entities—for
example, transliteration of from the Gujarati script to the Latin script “Ahmedabad” …

Pretrained models are a mainstay in modern NLP applications. Pretraining requires access
to large volumes of unlabeled text. While monolingual text is readily available for many of …