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Fine-Grained Named Entity Recognition (FG-NER) is critical for many NLPapplications. While classical named entity recognition (NER) has attracted asubstantial amount of research, FG-NER is still an open research domain. Thecurrent state-of-the-art (SOTA) model for FG-NER relies heavily on manualefforts for building a dictionary and designing hand-crafted features. Theend-to-end framework which achieved the SOTA result for NER did not get thecompetitive result compared to SOTA model for FG-NER. In this paper, weinvestigate how effective multi-task learning approaches are in an end-to-endframework for FG-NER in different aspects. Our experiments show that usingmulti-task learning approaches with contextualized word representation can helpan end-to-end neural network model achieve SOTA results without using anyadditional manual effort for creating data and designing features.

Multi-Task Learning with Contextualized Word Representations for Extented Named Entity Recognition