5 Key Advantages of Deep Learning in Natural Language Processing

1Compiled by New Intelligence

Source: machinelearningmastery.com

Author: Jason Brownlee

Compiled by: Zhu Huan

[New Intelligence Overview] In the field of Natural Language Processing (NLP), the promise of deep learning is: to bring better performance to new models that may require more data but no longer need as much linguistic expertise.

In the field of NLP, the promise of deep learning is: to bring better performance to new models that may require more data but no longer need as much linguistic expertise.

There is much hype and talk about deep learning methods, but beyond the hype, deep learning methods are achieving state-of-the-art results for challenging problems, especially in the field of NLP.

In this article, you will see the specific prospects of deep learning methods addressing NLP problems. After reading this article, you will know:

1. The promise of deep learning in NLP.

2. What deep learning practitioners and research scientists say about the promise of deep learning in NLP.

3. Important deep learning methods and applications in NLP.

Let’s get started.

The Promise of Deep Learning

Deep learning methods are popular mainly because they deliver on their initial promises.

This is not to say that there is no hype technically, but rather that this hype is based on very real results. These results are being validated across a range of challenging AI problems in computer vision and NLP.

The first large-scale demonstration of deep learning’s power was in the field of NLP, particularly in speech recognition. Recent advances have been in machine translation.

In this article, we will see five specific promises of deep learning methods in the field of NLP. These promises have been recently emphasized by researchers and practitioners in this field, and their attitudes towards these promises are much more restrained than typical news reports.

In summary, these promises are:

Deep Learning Replaces Existing Models. Deep learning methods can be inserted into existing NLP systems, resulting in new models that achieve equivalent or better performance.
New NLP Models. Deep learning methods provide new modeling approaches to tackle NLP problems (such as sequence-to-sequence prediction).
Feature Learning. Deep learning methods can learn features from the natural language required by the model without needing expert-specified feature extraction.
Continuous Improvement. The performance of deep learning in NLP is based on real-world results, and the improvements being brought are ongoing and may accelerate.
End-to-End Models. Large end-to-end deep learning models can adapt to NLP problems, providing more general and better approaches.

We will now take a closer look at each of these promises. In fact, there are some other promises of deep learning in NLP; these are just the five most prominent ones I have selected.

Deep Learning Replaces Existing Models

The first promise of deep learning in NLP is the ability to replace existing linear models with models that have better performance and can learn and leverage non-linear relationships.

Yoav Goldberg emphasizes in his “Neural Networks for NLP Researchers” that deep learning methods have achieved impressive results. He states in this article: “Recently, neural network models have also begun to be applied to natural language signals and have once again brought very promising results.”

He further emphasizes that these methods are easy to use and can sometimes be used to batch-replace existing linear methods. He says: “Recently, the field has seen some success in switching from sparse input linear models to dense data non-linear neural network models. Most neural network techniques are easy to apply and can sometimes almost replace old linear classifiers; however, there are still barriers to using neural networks in many cases.”

New NLP Models

Another promise is that deep learning methods aid in the development of entirely new models.

A good example is the use of recurrent neural networks that can learn and predict outputs for extremely long sequences. This approach is fundamentally different from previous ones as it allows NLP practitioners to move away from traditional modeling assumptions and achieve state-of-the-art results.

Yoav Goldberg points out in his book “Neural Network Methods in Natural Language Processing” that complex neural network models like recurrent neural networks can bring new modeling opportunities in NLP. He states, “Around 2014, the field began to see some success in the transition from sparse input linear models to dense input non-linear neural network models. Other changes are more advanced, requiring researchers to change their thinking and can lead to new modeling opportunities. In particular, a series of methods based on recurrent neural networks (RNNs) reduce the reliance on the Markov assumption that is prevalent in sequence models, allowing for conditioning on arbitrarily long sequences and producing effective feature extractors. These advances have led to breakthroughs in language modeling, automatic machine translation, and other applications.”

Feature Learning

Deep learning methods have the ability to learn feature representations without requiring experts to manually specify and extract features from natural language.

NLP researcher Chris Manning highlighted this aspect in the first lecture of his deep learning course for NLP.

He described the limitations of manually defined input features: in this approach, previous applications showed that machine learning merely validated features defined by humans in statistical NLP, and computers learned very little.

Chris believes that the promise brought by deep learning methods is automatic feature learning. He emphasizes that feature learning is automatic, not manual; it is easy to adapt, not fragile, and can continuously improve automatically.

Chris Manning stated in the first lecture slides of his “Natural Language Processing with Deep Learning” course in 2017, “Generally, the features we manually design are often over-specified, incomplete, take a long time to design and validate, and can leave you busy for a day only to achieve limited performance levels. In contrast, features learned by deep learning are easy to adapt, can be quickly trained, and can continuously learn to achieve better performance levels that were previously unattainable.”

Continuous Improvement

Another promise of deep learning in NLP is the continuous rapid improvement on challenging problems.

In the first lecture of the “Natural Language Processing with Deep Learning” course, Chris Manning stated that deep learning methods are popular because they are effective. He said, “The real reason deep learning is so exciting for most people is that it really works.”

He emphasized that the initial results of deep learning are impressive. Deep learning has outperformed any other methods in the past 30 years in the field of speech.

Chris mentioned that deep learning brings not only state-of-the-art results but also a rapid pace of ongoing improvement. He said, “In the past six or seven years, it has been astonishing how deep learning methods have continually improved and have become better at an amazing pace. I actually want to say this is unprecedented; I see the field progressing rapidly, with better methods being released every month.”

The Promise of End-to-End Models

The final promise of deep learning is the ability to develop and train end-to-end models for natural language problems, rather than developing processes for specialized models.

End-to-end models not only improve model performance but also provide better development speed and simplicity.

Neural Machine Translation (NMT) refers to the attempt to learn a large neural network that translates one language into another. Traditionally, this has been handled by a process consisting of a series of manually tuned models, each of which requires specialized linguistic knowledge.

Chris Manning described this in his lecture on NMT and attention models in Stanford’s NLP deep learning course. He said: “Neural machine translation is about building a large neural network where we can train the entire end-to-end machine translation process and optimize it. This trend away from manually customized models towards end-to-end, sequence-to-sequence prediction models has been a trend in speech recognition. Such systems are referred to as NMT (Neural Machine Translation) systems.”

Designing end-to-end models instead of developing processes for specialized systems is also a trend in speech recognition.

In the twelfth lecture of Stanford’s NLP course on end-to-end models for speech processing, NLP researcher Navdeep Jaitly, currently at Nvidia, emphasized that each component of speech recognition can be replaced with a neural network. Major components in the automatic speech recognition process include speech processing, acoustic models, pronunciation models, and language models. The challenge is that each component has different attributes and error types. This has spurred the need to develop a neural network to learn the entire problem end-to-end.

He said, “Over time, people have begun to notice that if we use neural networks, each of these components can perform better. However, there is still a problem. Each component has its own neural network, but the errors in each component are different, so they may not work well together. This motivates us to try to treat the entire speech recognition as a large model to train.”

Types of Deep Learning Networks in NLP

Deep learning is a large learning field, and not all of it is related to NLP.

What types of deep learning models can enhance performance? Learners can easily get bogged down in specific optimization methods.

At a higher level, there are five methods in deep learning that are most widely applied in NLP.

They are:

Embedding Layers
Multi-Layer Perceptrons (MLP)
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNNs)
Recursive Neural Networks (ReNNs)

Types of Problems in NLP

Deep learning will not completely solve NLP or AI problems.

So far, deep learning methods have been evaluated across a wide range of NLP problems and have achieved success in some of these problems. These successes indicate that using deep learning can achieve performance or capabilities higher than ever before.

Importantly, the areas where deep learning methods have achieved the most success are precisely those that are more user-facing, more challenging, and more interesting.

Five examples of deep learning success include:

Word Representation and Meaning
Text Classification
Language Modeling
Machine Translation
Speech Recognition

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