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An embarrassingly simple method to mitigate undesirable properties of pretrained language model tokenizers
We introduce FLOTA (Few Longest Token Approximation), a simple yet effective method to
improve the tokenization of pretrained language models (PLMs). FLOTA uses the …
improve the tokenization of pretrained language models (PLMs). FLOTA uses the …
A survey on syntactic processing techniques
Computational syntactic processing is a fundamental technique in natural language
processing. It normally serves as a pre-processing method to transform natural language …
processing. It normally serves as a pre-processing method to transform natural language …
Encoder-decoder methods for text normalization
Text normalization is the task of map** non-canonical language, typical of speech
transcription and computer-mediated communication, to a standardized writing. It is an up …
transcription and computer-mediated communication, to a standardized writing. It is an up …
Fortification of neural morphological segmentation models for polysynthetic minimal-resource languages
Morphological segmentation for polysynthetic languages is challenging, because a word
may consist of many individual morphemes and training data can be extremely scarce …
may consist of many individual morphemes and training data can be extremely scarce …
Morphological Processing of Low-Resource Languages: Where We Are and What's Next
Automatic morphological processing can aid downstream natural language processing
applications, especially for low-resource languages, and assist language documentation …
applications, especially for low-resource languages, and assist language documentation …
Canonical and surface morphological segmentation for Nguni languages
T Moeng, S Reay, A Daniels, J Buys - Southern African Conference for …, 2021 - Springer
Morphological Segmentation involves decomposing words into morphemes, the smallest
meaning-bearing units of language. This is an important NLP task for morphologically-rich …
meaning-bearing units of language. This is an important NLP task for morphologically-rich …
Deep convolutional networks for supervised morpheme segmentation of Russian language
A Sorokin, A Kravtsova - Artificial Intelligence and Natural Language: 7th …, 2018 - Springer
Deep Convolutional Networks for Supervised Morpheme Segmentation of Russian Language |
SpringerLink Skip to main content Advertisement Springer Nature Link Account Menu Find a …
SpringerLink Skip to main content Advertisement Springer Nature Link Account Menu Find a …
Tackling the low-resource challenge for canonical segmentation
Canonical morphological segmentation consists of dividing words into their standardized
morphemes. Here, we are interested in approaches for the task when training data is limited …
morphemes. Here, we are interested in approaches for the task when training data is limited …
Computational morphology with neural network approaches
L Liu - arxiv preprint arxiv:2105.09404, 2021 - arxiv.org
Neural network approaches have been applied to computational morphology with great
success, improving the performance of most tasks by a large margin and providing new …
success, improving the performance of most tasks by a large margin and providing new …
Convolutional neural networks for low-resource morpheme segmentation: baseline or state-of-the-art?
A Sorokin - Proceedings of the 16th Workshop on Computational …, 2019 - aclanthology.org
We apply convolutional neural networks to the task of shallow morpheme segmentation
using low-resource datasets for 5 different languages. We show that both in fully supervised …
using low-resource datasets for 5 different languages. We show that both in fully supervised …