Lecture

Natural Language Processing: Understanding Transformers and Tokenization

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Description

This lecture covers key concepts in Natural Language Processing (NLP), focusing on the analysis and synthesis of language. It begins with an overview of previous topics, including initialization and self-supervised learning. The instructor introduces the concept of tokens, explaining how text can be broken into smaller units for processing. The lecture delves into self-attention mechanisms, illustrating how models can focus on relevant parts of the input. The architecture of transformer models is discussed, highlighting the encoder and decoder models and their roles in generating useful text representations. The encoder-decoder model is explained in the context of sequence-to-sequence tasks, such as machine translation. The lecture also addresses the importance of tokenization, detailing various methods for transforming sequences of words into vectors for deep learning applications. Finally, the instructor emphasizes the significance of understanding these concepts for developing effective NLP systems, providing insights into practical applications and future directions in the field.

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Ontological neighbourhood

Information engineering

Natural language processing: Topics in natural language processing

Machine learning: Artificial neural networks

Related concepts (32)

Word embedding

In natural language processing (NLP), a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that words that are closer in the vector space are expected to be similar in meaning. Word embeddings can be obtained using language modeling and feature learning techniques, where words or phrases from the vocabulary are mapped to vectors of real numbers.

Machine translation

Machine translation is use of either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches to translation of text or speech from one language to another, including the contextual, idiomatic and pragmatic nuances of both languages. History of machine translation The origins of machine translation can be traced back to the work of Al-Kindi, a ninth-century Arabic cryptographer who developed techniques for systemic language translation, including cryptanalysis, frequency analysis, and probability and statistics, which are used in modern machine translation.

Statistical machine translation

Statistical machine translation (SMT) was a machine translation approach, that superseded the previous, rule-based approach because it required explicit description of each and every linguistic rule, which was costly, and which often did not generalize to other languages. Since 2003, the statistical approach itself has been gradually superseded by the deep learning-based neural network approach. The first ideas of statistical machine translation were introduced by Warren Weaver in 1949, including the ideas of applying Claude Shannon's information theory.

Neural machine translation

Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model. They require only a fraction of the memory needed by traditional statistical machine translation (SMT) models. Furthermore, unlike conventional translation systems, all parts of the neural translation model are trained jointly (end-to-end) to maximize the translation performance.

Recurrent neural network

A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

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