  Developing Named Entity Recognition (NER) systems for Indian languages has
been a long-standing challenge, mainly owing to the requirement of a large
amount of annotated clean training instances. This paper proposes an end-to-end
framework for NER for Indian languages in a low-resource setting by exploiting
parallel corpora of English and Indian languages and an English NER dataset.
The proposed framework includes an annotation projection method that combines
word alignment score and NER tag prediction confidence score on source language
(English) data to generate weakly labeled data in a target Indian language. We
employ a variant of the Teacher-Student model and optimize it jointly on the
pseudo labels of the Teacher model and predictions on the generated weakly
labeled data. We also present manually annotated test sets for three Indian
languages: Hindi, Bengali, and Gujarati. We evaluate the performance of the
proposed framework on the test sets of the three Indian languages. Empirical
results show a minimum 10% performance improvement compared to the zero-shot
transfer learning model on all languages. This indicates that weakly labeled
data generated using the proposed annotation projection method in target Indian
languages can complement well-annotated source language data to enhance
performance. Our code is publicly available at
https://github.com/aksh555/CL-NERIL
