Papers with historical text normalization

3 papers
Historical Text Normalization with Delayed Rewards (P19-1)

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Challenge: Recent work on a novel approach to historical text normalization has shown that policy gradient fine-tuning improves accuracy across languages.
Approach: They propose to train sequence-to-sequence models with simple token-level log-likelihood with reinforcement learning to optimize for exact matches.
Outcome: The proposed model outperforms phrase-based models in the evaluation metric for historical text normalization across languages.
Applying the Transformer to Character-level Transduction (2021.eacl-main)

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Challenge: morphological inflection generation and historical text normalization tasks are character-level tasks that outperform recurrent models.
Approach: They propose a technique to handle feature-guided character-level transduction that further improves performance.
Outcome: The transformer outperforms recurrent models on morphological inflection and historical text normalization tasks.
A Large-Scale Comparison of Historical Text Normalization Systems (N19-1)

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Challenge: a large study of historical text normalization is done on eight languages . there is no consensus on the state-of-the-art approach to normalization .
Approach: They present a large study of historical text normalization done on eight languages . they evaluate four different systems based on supervised learning on datasets from eight different languages based in the literature .
Outcome: The proposed methods are based on supervised learning and are available online.

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