A model corrects Lithuanian grammatical errors.
problem Lack of language skills and typing errors in Lithuanian.
method Transformer architectures for subword and byte-level approaches.
result F=0.92 for Lithuanian grammatical error correction.
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5 results for “byte-level”
Byte-level machine translation outperforms embedding-based methods.
problem Improving machine translation without embedding layers.
method Replacing embedding layers with one-hot representations of bytes, and using decoder-input dropout.
result Byte-to-byte machine translation achieves BLEU scores comparable to character-level and subword-level models.
We introduce multiplicative LSTM (mLSTM), a recurrent neural network architecture for sequence modelling that combines the long short-term memory (LSTM) and multiplicative recurrent neural network architectures. mLSTM is characterised by its ability to have different recurrent transition functions for each possible inp…
A new transformer model corrects diacritics and typos in multiple languages.
problem Restoring diacritics and correcting typos in online communications.
method Employing a universal ByT5 transformer model trained on 12 languages, including Lithuanian.
result Achieves > 98% accuracy in diacritics restoration and typos correction.
ByteGen models LOB dynamics without tokenization, achieving realistic market metrics.
problem Modeling high-frequency LOB dynamics in finance.
method Autoregressive next-byte prediction on packed binary data, using H-Net architecture.
result Successfully reproduces stylized facts of financial markets.