Semi-supervised Contextual Historical Text Normalization
Peter Makarov, Simon Clematide
Phonology, Morphology and Word Segmentation Long Paper
Session 12B: Jul 8
(09:00-10:00 GMT)
Session 13B: Jul 8
(13:00-14:00 GMT)
Abstract:
Historical text normalization, the task of mapping historical word forms to their modern counterparts, has recently attracted a lot of interest (Bollmann, 2019; Tang et al., 2018; Lusetti et al., 2018; Bollmann et al., 2018;Robertson and Goldwater, 2018; Bollmannet al., 2017; Korchagina, 2017). Yet, virtually all approaches suffer from the two limitations: 1) They consider a fully supervised setup, often with impractically large manually normalized datasets; 2) Normalization happens on words in isolation. By utilizing a simple generative normalization model and obtaining powerful contextualization from the target-side language model, we train accurate models with unlabeled historical data. In realistic training scenarios, our approach often leads to reduction in manually normalized data at the same accuracy levels.
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