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Diacritics Restoration using BERT with Analysis on Czech language

2021-05-24 · Jakub Náplava, Milan Straka, Jana Straková

We propose a new architecture for diacritics restoration based on contextualized embeddings, namely BERT, and we evaluate it on 12 languages with diacritics. Furthermore, we conduct a detailed error analysis on Czech, a morphologically rich language with a high level of diacritization. Notably, we manually annotate all mispredictions, showing that roughly 44% of them are actually not errors, but either plausible variants (19%), or the system corrections of erroneous data (25%). Finally, we categorize the real errors in detail. We release the code at https://github.com/ufal/bert-diacritics-restoration.

📄 PDF Abstract BibTeX arXiv:2105.11408

Code (1)

ufal/bert-diacritics-restoration 공식 구현 pytorch

Tasks

Croatian Text DiacritizationCzech Text DiacritizationFrench Text DiacritizationHungarian Text DiacritizationIrish Text DiacritizationLatvian Text DiacritizationPolish Text DiacritizationRomanian Text DiacritizationSlovak Text DiacritizationSpanish Text DiacritizationTurkish Text DiacritizationVietnamese Text Diacritization

Methods 이 논문이 사용한 방법론

Multi-Head Attention 설명 없음
Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
Linear Warmup With Linear Decay Linear Warmup With Linear Decay is a learning rate schedule in which we increase the learning rate linearly for $n$ updates and then linearly decay afterwards.
WordPiece 설명 없음
Layer Normalization Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs…
Attention Dropout Attention Dropout is a type of dropout used in attention-based architectures, where elements are randomly dropped out of the…
Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…
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