Semi-supervised Contextual Historical Text Normalization
Peter Makarov, Simon Clematide · 2020
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;Bollmann et 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.