| Challenge: | Historical text normalization is the task of mapping historical word forms to their modern counterparts. |
| Approach: | They propose to use a generative normalization model to obtain contextualization from the target-side language model. |
| Outcome: | et al., 2018) show that the most effective approach reduces manual normalization time and manual training costs. |
Similar Papers
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. |
Evaluating Historical Text Normalization Systems: How Well Do They Generalize? (N18-2)
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| Challenge: | Historical text normalization systems aim to convert historical wordforms to their modern equivalents . many of these systems have been developed and tested on a single language . |
| Approach: | They propose to use a nave baseline system to evaluate historical text normalization systems . they show that the models generalize well to unseen words in tests on five languages . |
| Outcome: | The proposed models generalize well to unseen words on five languages, but provide no clear benefit over the nave baseline. |
Context-Aware Text Normalisation for Historical Dialects (2020.coling-main)
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| Challenge: | Context-aware historical text normalisation is a severely under-researched area . a new approach to normalise historical spellings relies on the state-of-the-art methods . |
| Approach: | They propose a multidialect normaliser with a context-aware reranking approach . they incorporate dialectal information into the training and use a word-level n-gram language model . |
| Outcome: | The proposed approach improves accuracy on historical datasets and further improves on baseline. |
Few-Shot and Zero-Shot Learning for Historical Text Normalization (D19-61)
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| Challenge: | Historical text normalization often relies on small training datasets. |
| Approach: | They evaluate 63 multi-task learning configurations for sequence-to-sequence-based historical text normalization across ten datasets from eight languages. |
| Outcome: | The proposed learning architecture outperforms the simple, but strong identity baseline. |
Synthetic Data for English Lexical Normalization: How Close Can We Get to Manually Annotated Data? (2020.lrec-1)
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| Challenge: | Social media data is a valuable data resource for natural language processing tasks. |
| Approach: | They propose to adapt input text to a more standard form, a task also referred to as normalization. |
| Outcome: | The proposed system scores 94.29 accuracy on the test data compared to 95.22 when trained on human-annotated data. |
Dialect-to-Standard Normalization: A Large-Scale Multilingual Evaluation (2023.findings-emnlp)
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| Challenge: | Text normalization is a range of tasks that consist in replacing non-standard spellings with their standard equivalents. |
| Approach: | They introduce dialect-to-standard normalization as a sentence-level character transduction task and provide a large-scale analysis of these methods. |
| Outcome: | The proposed model performs best for Finnish, Swiss German and Slovene while the pre-trained model using full sentences performs the best for Norwegian. |
Handling Normalization Issues for Part-of-Speech Tagging of Online Conversational Text (L18-1)
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Géraldine Damnati, Jeremy Auguste, Alexis Nasr, Delphine Charlet, Johannes Heinecke, Frédéric Béchet
| Challenge: | a new approach to POS tagging noisy user generated text is proposed . word embeddings are trained on a noisy corpus to address both normalization and POS. |
| Approach: | They propose to use word embeddings to normalize text before tagging it, while a gated neural network based tagger handles the remaining errors. |
| Outcome: | The proposed approach normalizes some errors before tagging, while a gated neural network handles the remaining errors. |
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. |
Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems (2021.naacl-industry)
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| Challenge: | Developing Text Normalization systems for Text-to-Speech (TTS) on new languages is hard. |
| Approach: | They propose a novel architecture to facilitate Text Normalization systems for TTS on new languages . they use a granular tokenization mechanism that enables the system to learn majority of classes . |
| Outcome: | The proposed architecture performs comparable with the state-of-the-art systems on English . the proposed system learns most classes from training data and precodes them for other classes . |
Comprehensive Evaluation on Lexical Normalization: Boundary-Aware Approaches for Unsegmented Languages (2025.findings-emnlp)
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| Challenge: | Lexical normalization research has sought to tackle the challenge of processing informal expressions in user-generated text. |
| Approach: | They focus on Japanese normalization and developing methods based on state-of-the-art pre-trained models . |
| Outcome: | The proposed methods achieve high accuracy and efficiency across multiple evaluation perspectives. |