Understanding The Difference Between Deep Learning And NLP

This article discusses the difference between deep learning and NLP (natural language processing), as well as common deep learning frameworks for NLP applications.

Deep Learning in NLP: The Foundation

Neural networks have been rebranded as deep learning, a subfield of machine learning. In the past, these methods were influenced by the way the brain performs calculation. Deep learning techniques emphasize both learning to accurately represent the data in a way that is appropriate for prediction as well as making predictions based on historical observations. This is accomplished by feeding data into a network that uses chained layers of differentiable functions to alter the input data successively. The term “deep learning” refers to the fact that these algorithms are connected together in multiple layers. The network handles a large portion of the intricate process of learning the correct representation automatically, while the human designer configures the network architecture and training.

The application of neural networks to natural language problems is quite attractive. The usage of an embedding layer, which converts discrete symbols such as words to continuous vectors in a comparatively low-dimensional space, is a crucial element. This helps with generalization by converting words from discrete symbols into mathematical objects where vector distance can relate to word distance. The problems of data sparsity and discreteness are lessened by this approach.

Features were frequently sparse and high-dimensional in classical NLP with linear models, with each feature having a unique dimension. A major change to dense representations, where every feature is mapped to a vector, is required when moving to deeper neural networks. The emphasis is on extracting essential features rather than several feature combinations.

A foundation in mathematics, such as calculus, linear algebra (vectors, matrices, derivatives, partial derivatives), probability, and statistics (conditional probabilities, independent events), is usually necessary to comprehend deep learning ideas for natural language processing. Gradient descent and other optimization techniques are frequently used in training, which computes gradients on a computation graph using error backpropagation.

Typical Deep Learning Frameworks for NLP Applications

For challenges like translating discrete token sequences, neural networks offer general methods. In NLP, a few architectures have shown exceptional efficacy:

Multi-layer perceptron’s (MLPs) and feedforward neural networks (FFNNs)

• Unlike linear models, which can only describe linear relations, these networks are universal approximators that can represent any Borel-measurable function.
• They are made up of layers of neurons arranged into three groups: input, output, and one or more hidden layers. A nonlinear activation function usually follows each buried layer.
• Tasks like language modeling and word prediction based on past context can be accomplished with FFNNs. They serve as a probabilistic classifier in this application, calculating the likelihood of the subsequent word.
• The data point is represented as a vector that populates the input layer.
• As the neural counterpart of a bag-of-words model, the Deep Averaging Network (DAN) is a straightforward text classification method. A single vector is created by averaging static word embeddings, which is then fed via one or more intermediary neural layers.

CNNs, or convolutional neural networks

• The learning of informative grammar patterns in text is the focus of these networks.
• Because CNNs can learn to identify local indications, such as key phrases, independent of their location in the input, they are useful for classification problems where pertinent clues may exist anywhere in the input.
• In order to create a fixed-size vector representation of the structure, they are made to find indicative local predictors and combine them. This implies that pre-specified embeddings for each potential n-gram are not necessary for the architecture to recognize predictive n-grams.
• Document classification, short-text categorization, sentiment classification, relation-type classification, event detection, paraphrase identification, semantic role labeling, and question answering are among the areas where CNNs have demonstrated encouraging performance.
• CNNs are frequently employed as feature extractors, generating vectors that are subsequently supplied to other network components for prediction.
• They can occasionally provide notable speed improvements over LSTMs on GPUs for tasks like Named Entity Recognition (NER), which involves text categorization and sequence tagging.

RNNs, or recurrent neural networks

• RNNs are made to process sequential data and identify minute regularities and patterns. By examining the “infinite windows” surrounding a word, they enable the modeling of non-markovian relationships.
• They can use fixed-size vectors to represent sequential inputs of any size.
• RNNs are regarded as a significant contribution of deep learning to statistical NLP because of their ability to effectively capture statistical regularities in sequential inputs.
• They can be employed as acceptors, reading a sequence of inputs and ultimately generating a binary or multi-class response.
• Language modeling, sequence classification tasks like sentiment analysis, and sequence labeling tasks like Part-of-Speech (POS) tagging are all areas where RNNs are used.
• Long inputs cause problems with simple RNNs, such as vanishing gradients; more sophisticated gated designs are primarily used in recent systems.

LSTM and GRU gated architectures

• Concrete implementations of the RNN abstraction include the Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM).
• These models solve the vanishing gradient issue of simple RNNs by explicitly choosing which information to remember and which to forget in their hidden and context layers. They may be deep learning’s most significant addition to the statistical NLP toolkit.
• Language modeling, sequence labeling, and sequence classification are prominent applications for LSTMs and GRUs.
• Because they can integrate data from both past and future contexts, bidirectional LSTMs are widely used for sequence labeling tasks such as POS tagging and NER.

Models of encoder-decoders

• Usually, this design makes use of two distinct RNN models. An input sequence is mapped to an intermediate representation known as the context vector by one RNN, the encoder. This context vector is mapped to an output sequence by the decoder, the second RNN.
• The primary modeling method used in state-of-the-art machine translation is encoder-decoder models.
• They can also be included into task-oriented dialogue systems, in which the decoder creates the system’s answer after the encoder evaluates user input.

Mechanism of Attention

• Neural networks, frequently with encoder-decoder models, employ attention as a way to help the model concentrate on pertinent segments of the input sequence when producing output or making predictions.
• It was created to enhance encoder-decoder RNN models’ performance.
• The most advanced machine translation systems available today are driven by attention-based models.
• Other structures, like converting a DAN’s simple average into a weighted one, can also make advantage of attention.

Transformers

• One popular contemporary architecture for language modeling is the transformer.
• To capture the links and sequence structure between words, especially over vast distances, they use new processes like as positional encodings and self-attention.
• Transformers such as BERT, GPT, and ALBERT are now commonplace. A masked language modeling architecture serves as the foundation for BERT.
• For applications like language modeling or masked language modeling, a significant paradigm is transfer learning, in which big transformer models are pretrained on enormous text corpora.
• By further training the model, frequently with additional task-specific layers, these pretrained models can then be optimized for certain downstream NLP tasks (such as text categorization, question answering, and NER). Pretraining is thought to acquire rich language representations that make learning tasks later on easier.
• By overlaying the representation of a specific [CLS] token with an output layer, transformers can be used directly for tasks such as text classification.
• For tasks like POS tagging, they frequently work with sub-word units rather than entire words, necessitating label mapping.
• Applications like chatbots, conversational AI, and text/code generation are made possible by large language models (LLMs), which are frequently built on transformer architectures. To cut down on sequential computation in LLMs, optimizations such as FFN Fusion are being investigated.
• Many modern NLP systems are built on top of these architectures, especially LSTMs/GRUs and the Transformer family, which handle a variety of jobs from simple sequence tagging to intricate generation and comprehension issues.

Difference between Deep learning and NLP