| 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. |
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Semi-supervised Contextual Historical Text Normalization (2020.acl-main)
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| 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. |
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. |
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. |
Revisiting Context Choices for Context-aware Machine Translation (2024.lrec-main)
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| Challenge: | Recent work has cast doubt on whether context-aware machine translation models learn useful signals from context or are improvements in automatic evaluation metrics just a side-effect. |
| Approach: | They propose to use separate encoders for source sentence and context as multiple sources for one target sentence to train context-aware machine translation models. |
| Outcome: | The proposed model improves translation quality even with empty lines as context, but the correct context improves it and random out-of-domain context degrades it. |
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. |
MoNoise: A Multi-lingual and Easy-to-use Lexical Normalization Tool (P19-3)
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| Challenge: | In this paper, we demonstrate the online demo and command line interface of a lexical normalization system (MoNoise) for a variety of languages. |
| Approach: | They propose to bundle seven datasets in six languages to form a new benchmark and a novel evaluation metric which is particularly suitable for cross-dataset comparisons. |
| Outcome: | The proposed model is based on the original word and features from the original language for each normalization candidate. |
Norm-based Noisy Corpora Filtering and Refurbishing in Neural Machine Translation (2022.emnlp-main)
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| Challenge: | Existing noisy corpora filtering methods are insufficient to solve this problem, requiring multiple scorers trained on clean bitexts. |
| Approach: | They propose to use the information ratio from the source to the target side to distinguish unparallel sentence pairs by using norms of context vectors. |
| Outcome: | The proposed method performs comparably with state-of-the-art noisy corpora filtering techniques but is more efficient and easier to operate. |
Challenges in Context-Aware Neural Machine Translation (2023.emnlp-main)
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| Challenge: | despite well-reasoned intuitions, most context-aware neural machine translation models show only modest improvements over sentence-level systems. |
| Approach: | They propose a more realistic setting for document-level translation called paragraph-to-paragraph (PARA2PARA) they collect a dataset of Chinese-English novels to promote future research . |
| Outcome: | The proposed model improves translation quality across document-level metrics and discourse phenomena. |
Data augmentation using back-translation for context-aware neural machine translation (D19-65)
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| Challenge: | A single sentence does not always convey information that is enough to translate it into other languages. |
| Approach: | They obtain large-scale pseudo parallel corpora by back-translating monolingual data and examine their impact on translation accuracy. |
| Outcome: | The large-scale pseudo parallel corpora obtained by back-translating monolingual data showed that the model trained with small parallel corporeals and large-sized pseudo parallels improved translation accuracy. |