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Deep linguistic processing is a natural language processing framework which draws on theoretical and descriptive linguistics. It models language predominantly by way of theoretical syntactic/semantic theory (e.g. CCG , HPSG , LFG , TAG , the Prague School ).
Data-driven learning (DDL) is an approach to foreign language learning. Whereas most language learning is guided by teachers and textbooks, data-driven learning treats language as data and students as researchers undertaking guided discovery tasks. Underpinning this pedagogical approach is the data - information - knowledge paradigm (see DIKW ...
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.
The direct method operates on the idea that second language learning must be an imitation of first language learning, as this is the natural way humans learn any language: a child never relies on another language to learn its first language, and thus the mother tongue is not necessary to learn a foreign language. This method places great stress ...
The students begin learning the language by work on its sound system. The sounds are associated with different colors on a sound-color chart that is specific to the language being learned. The teacher first elicits sounds that are already present in the students' native language, and then progresses to the development of sounds that are new to ...
The design has its origins from pre-training contextual representations, including semi-supervised sequence learning, [24] generative pre-training, ELMo, [25] and ULMFit. [26] Unlike previous models, BERT is a deeply bidirectional, unsupervised language representation, pre-trained using only a plain text corpus.
Developmental linguistics is the study of the development of linguistic ability in an individual, particularly the acquisition of language in childhood. It involves research into the different stages in language acquisition, language retention, and language loss in both first and second languages, in addition to the area of bilingualism. Before ...
It is named "chinchilla" because it is a further development over a previous model family named Gopher. Both model families were trained in order to investigate the scaling laws of large language models. [2] It claimed to outperform GPT-3. It considerably simplifies downstream utilization because it requires much less computer power for ...