Subrata Mitra


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Few-Shot Class-Incremental Learning for Named Entity Recognition
Rui Wang | Tong Yu | Handong Zhao | Sungchul Kim | Subrata Mitra | Ruiyi Zhang | Ricardo Henao
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Previous work of class-incremental learning for Named Entity Recognition (NER) relies on the assumption that there exists abundance of labeled data for the training of new classes. In this work, we study a more challenging but practical problem, i.e., few-shot class-incremental learning for NER, where an NER model is trained with only few labeled samples of the new classes, without forgetting knowledge of the old ones. To alleviate the problem of catastrophic forgetting in few-shot class-incremental learning, we reconstruct synthetic training data of the old classes using the trained NER model, augmenting the training of new classes. We further develop a framework that distills from the existing model with both synthetic data, and real data from the current training set. Experimental results show that our approach achieves significant improvements over existing baselines.


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Parsing Generalized Phrase Structure Grammar with Dynamic Expansion
Navin Budhiraja | Subrata Mitra | Harish Karnick | Rajeev Sangal
Proceedings of the First International Workshop on Parsing Technologies

A parser is described here based on the Cocke-Young-Kassami algorithm which uses immediate dominance and linear precedence rules together with various feature inheritance conventions. The meta rules in the grammar are not applied beforehand but only when needed. This ensures that the rule set is kept to a minimum. At the same time, determining what rule to expand by applying which meta-rule is done in an efficient manner using the meta-rule reference table. Since this table is generated during “compilation” stage, its generation does not add to parsing time.