3260 papers • 126 benchmarks • 313 datasets
Bangla spell checker which improves the quality of suggestions for misspelled words.
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It is conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and it is proposed to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
This work introduces an architecture based entirely on convolutional neural networks, which outperform the accuracy of the deep LSTM setup of Wu et al. (2016) on both WMT'14 English-German and WMT-French translation at an order of magnitude faster speed, both on GPU and CPU.
A novel detector-purificator-corrector framework based on denoising transformers that outperforms previous state-of-the-art methods by a significant margin for Bangla spelling error correction and presents a method for large-scale corpus creation from scratch which in turn resolves the resource limitation problem of any left-to-right scripted language.
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