Perfetti & Stafura, 2014), which consists of many subcomponents. Vocabulary knowledge is a central element of the comprehension process (cf. Studying the component skills that constitute or support comprehension may help explain these individual differences and serve as a starting point to guide intervention efforts. Nevertheless, the levels of reading comprehension that children attain vary, even setting aside problems related to specific reading difficulties and more general language impairments. Reading comprehension is a central skill taught to school-age children, serving as a means of knowledge transfer that becomes increasingly important throughout their school careers and beyond. Monolinguals and bilinguals showed similar performance on almost all language measures, including semantic priming and reading comprehension. Delayed facilitative priming effects were observed for non-associated context-dependently and context-independently related word pairs, but these were not linked to individual differences in reading comprehension. A self-paced reading experiment involving both associated and non-associated, context-dependent (functional) and context-independent (categorical) semantic relations was administered to 137 Dutch monolingual and bilingual children. In the present study, we investigated whether priming during sentence reading, rather than single word priming, could be related to children's reading comprehension scores. However, other studies have not been able to replicate these effects on an individual differences level, even though the spreading of semantic activation is hypothesized to play a role in the reading comprehension process. MONOLINGUAL IN A SENTENCE HOW TOThese properties lead to higher costs due to the larger amount of data and time resources needed.ĭue to this fact, I am going to show you how to train a monolingual non-English BERT-based multi-class text classification model.Group differences in semantic priming between young readers with different comprehension levels have been reported, with poor readers showing reduced or no context-independent semantic priming compared to normal readers. But these models are bigger, need more data, and also more time to be trained. Multilingual models are already achieving good results on certain tasks. Monolingual models, as the name suggest can understand one language. This model supports and understands 104 languages. An example of a multilingual model is mBERT from Google research. Multilingual models describe machine learning models that can understand different languages. “multilingual, or not multilingual, that is the question” - as Shakespeare would have said You can build either monolingual models or multilingual models. In deep learning, there are currently two options for how to build language models. If you haven’t, or if you’d like a refresh, I recommend reading this paper. Opening my article let me guess it’s safe to assume that you have heard of BERT.
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