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Learning attention for historical text normalization by learning to pronounce
2017
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Automated processing of historical texts often relies on pre-normalization to modern word forms. Training encoder-decoder architectures to solve such problems typically requires a lot of training data, which is not available for the named task. We address this problem by using several novel encoder-decoder architectures, including a multi-task learning (MTL) architecture using a grapheme-to-phoneme dictionary as auxiliary data, pushing the state-of-theart by an absolute 2% increase in
doi:10.18653/v1/p17-1031
dblp:conf/acl/BollmannBS17
fatcat:7bkzauziknenrpcx5toz7wxndm