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INTRODUCTION TO SELF-SUPERVISED LEARNING IN NLP

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Self-supervised learning (SSL) is a prominent part of deep learning. This is a
legit method that is used to train most of the models as it can learn from the
unlabeled data, making it easier to leverage a larger volume of raw data. But
how is it done?

When neural networks are provided with data, these networks try to connect the
patterns within the data and extract relevant features. These features are then
used to make decisions, like classifying objects (image classification),
predicting a number (regression), generating captions (caption generator), and
more.

In this blog, we will discuss some techniques by which state-of-the-art deep
learning models can be trained with less time and resources. We will also look
into the details of self-supervised learning, its types, and the applications in
which these models are used.


TABLE OF CONTENTS

 * 1. Transfer learning
 * 2. Self-supervised learning
 * 2.1. Supervised learning
 * 2.2. Unsupervised learning
 * 2.3. Semi-supervised learning
 * 2.4. Reinforcement learning
 * 3. Difference between supervised and unsupervised learning
 * 4. Why was self-supervised learning introduced?
 * 5. Self-supervised learning applications in NLP
 * 5.1. Next sentence prediction
 * 5.2. Auto-regressive language modeling
 * 6. Wrapping up


TRANSFER LEARNING



Recently the IT industry saw an increase in the use of deep learning methods,
thanks to the available large amounts of data and enough compute/processing
power. This resulted in the training of heavier deep learning models on much
larger datasets, particularly in computer vision and natural language processing
tasks.

Therefore, the output models, which achieved state-of-the-art results on
standard benchmarks, were made available as pre-trained models for use. It was
mainly used to fine-tune custom datasets. This technique forms the basis for
transfer learning.

Transfer learning is defined as transferring the knowledge gained by a model
while learning one task. Then these trained models can be applied to a similar
task with little modifications. You can also use pre-trained models like
Resnet50, EfficientNet, and more. These models are trained on millions of images
(like the ImageNet dataset) and fine-tuned on your data.

Let's consider an example to train an image classification model of Cats vs Dogs
(i.e., PETs Dataset). You can use the ResNet50 pre-trained model that has been
trained on the Imagenet dataset with 1000 classes. Use the model to fine-tune
the PETS dataset, containing different images of cats and dogs.

Since you are using the pre-trained model, you are not starting the model
training from scratch which means that the model has some knowledge about the
appearance of a cat or dog. Now you can fine-tune the pre-trained model with the
dataset and achieve good results quickly as compared to the training model from
scratch.

But you can't use transfer learning when you have no pre-trained models
available. Here, you can use the self-supervised learning technique to train
models and produce accurate results in lesser time and with minimum resources.


SELF-SUPERVISED LEARNING



Self-supervised learning is a technique used to train models in which the output
labels are a part of the input data, thus no separate output labels are
required. It is also known as predictive learning or pretext learning. In this
method, the unsupervised problem is changed into a supervised one using
auto-generation of labels. A classic example of this would be language models.

The language model is a word sequence prediction model trained to predict the
next word based on the previous input sentence. This kind of task is a
self-supervised learning task because you are not defining separate output
labels. Instead, you are providing the texts as inputs and outputs in a specific
way that will help the model to understand the fundamentals and style of the
language used in the dataset (or the language used in the dataset).

Self-supervised learning is combined with transfer learning to create a more
advances NLP model. When you don't have any pre-trained models for our dataset,
you can create one using self-supervised learning. You can train a language
model using the text corpus available in the train and test dataset.

You can train a language model by providing an independent variable like the
text of a specific length, and then provide the same text by appending the next
word as the output label. This works when a lot of text is provided in the model
and it can easily find patterns and learn the basic style of the text.

In this way, the model will learn to predict the next word in the sentence.
Usually, the language model generates text but it is not useful for any
downstream task until it is fine-tuned.

The language model acts as a pre-trained model that can be used to conduct
transfer learning i.e., fine-tuning with the same dataset or different datasets
for any downstream tasks like text classification, sentiment analysis, and more.

One of the best resources to find the language models is HuggingFace. It has
many models trained using corpora of text data of different styles and
languages. Machine learning algorithms are broadly classified as

 1. Supervised
 2. Semi-supervised
 3. Unsupervised
 4. Reinforcement learning

Let's see what each of these means in brief:


SUPERVISED LEARNING

The supervised learning technique is a popular technique that helps with
training your neural networks on labeled data for a specific task. In this
technique, a machine learning model will have inputs and corresponding labels to
learn about.

For example, you can consider image classification, regression analysis, or
more. For instance, take a classroom where a student is learning from the
teacher about any concept with different examples for better understanding.


UNSUPERVISED LEARNING

A deep learning technique that is used to find data from implicit patterns
without explicitly training on labeled data is known as unsupervised learning
models. Contrary to the supervised learning models, it doesn’t need a feedback
loop and annotations for training.

In this technique, a machine learning model will get inputs and the model will
find patterns from it and use those to predict the output. For example,
clustering, and principal component analysis all come under this.




SEMI-SUPERVISED LEARNING

It is a combination of both unsupervised and supervised learning models. This
technique comes in handy when you have a small set of labeled data points for
training the model. With the training process, you can use a pseudo-label and a
set of labeled data for the rest of the dataset.

For instance, when a student learns how to deal with specific problems from
their teacher, they have to figure out how to solve those problems by
themselves. It is a semi-supervised learning model.


REINFORCEMENT LEARNING

It is a method used for training AI agents to learn environmental behavior in
certain contexts with the help of a reward feedback policy. With this technique,
a machine learning model learns from actions and rewards.

It depends on how agents take action in an environment for maximizing the awards
received. Examples of this learning model are path planning, chess engine, a
child who wants to win a stage game, and more.


DIFFERENCE BETWEEN SUPERVISED AND UNSUPERVISED LEARNING

Both supervised and unsupervised learning have distinct objectives and provide
you with distinct solutions as per your requirement.

Supervised learning models and unsupervised learning models can be complementary
learning models as both don’t require labeling of the datasets. Unsupervised
learning models must be the superset of self-supervised learning as they don’t
provide any feedback loops.

Supervised LearningUnsupervised LearningInput dataLabelledUnlabelledFeedback
MechanismAvailableNot availableData ClassificationDone based on the training
datasetAssigns properties of a given data for classificationTypesRegression and
ClassificationClustering and AssociationUsageFor predictionFor
analysisAlgorithmsDecision trees, Support Vector Machine, Logistic
RegressionHierarchical Clustering, K-means Clustering, Apriori
AlgorithmClassesKnown number of classesAn unknown number of classes



On the other hand, the self-supervised learning model has many supervisory
signals which act as responses in the process of the training. An unsupervised
learning model focuses more on the model and not on the data. In contrast, the
supervised learning model works the other way.

But, unsupervised learning models are exceptional at dimensionality reduction
and clustering, whereas supervised learning models are a pretext technique for
classification and regression tasks. Another major difference is that supervised
learning functions around labeled data but unsupervised learning mainly deals
with unlabeled data.


WHY WAS SELF-SUPERVISED LEARNING INTRODUCED?

Self-supervised learning model was designed to address the following common
issues.

 * High Cost: To acquire labeled data, it was necessary to use more of the
   learning models. However, to acquire higher quality labeled data, you have to
   pay a higher price and the task might also be tedious and time-consuming.
 * Generic AI: The self-supervised learning model framework is closely related
   to embedding human cognition with machines. So the self-supervised machine is
   also considered artificial intelligence.
 * Lengthy lifecycle: While developing machine learning models, the data
   preparation lifecycle will be a highly lengthy process. You will need to
   filter, annotate, review, clean, and restructure the data according to your
   training framework.

The self-supervised learning model applications first came into existence
because of the above-mentioned concerns. It not only overcomes the above
concerns but also provides additional benefits like flexibility, and integrity
of data which all come at a lower cost.


SELF-SUPERVISED LEARNING APPLICATIONS IN NLP

SSL created huge steps in the NLP (Natural Language Processing) field. The
self-supervised learning is widely used everywhere starting from application
documentation processing, sentence completion, text suggestions, and more.

But, the learning abilities of the self-supervised model evolves majorly after
the release of the Word2Vec research paper, which took the natural language
processing domain to the next level. The model can predict the next word
depending on the prior pattern, this is the idea behind word embedding
approaches.

It is because of such improvements that came from the Word2Vec research paper,
you will now be able to get a beneficial representation using the allotment of
word-embedded systems which are used for scenarios like word prediction,
sentence completion, and so on. BERT (Bidirectional Encoder Representations from
Transformers) is one of the most eminent SSL methods that is used in natural
language processing.

Now, let us discuss some of the vital applications of self-supervised learning
models:


NEXT SENTENCE PREDICTION

With next sentence prediction, you can pick up two concurrent sentences from a
document and an unspecified sentence from the same or different document, so you
have a sentence 1, sentence 2, and sentence 3.

Then, you can ask the self-supervised learning model the relative position of
sentence 1 to sentence 2, and the model will provide an output either with
IsNotNextSentence or IsNextSentence. You can use the same self-supervised model
with all the combinations.

You should consider the below scenarios:

 1. The mission to the moon is finally starting after so many years.
 2. You can watch TV when you reach home.
 3. You can go home after you come from school.



When you ask an individual to reorder any different sentences which will be
suitable for our logical understanding, they would mostly choose sentence 1 and
2 as one after the other. The vital reason for using this model is to predict
sentences depending on the everlasting contextual dependencies.

BERT published a paper written by Google’s artificial intelligence team of
researchers who have proficient in various NLP tasks like natural language
inference, question answering, and many more.

For similar tasks, BERT will provide a great method for capturing the
relationships between sentences that is not possible with other language
modeling methods. Here is how the self-supervised model for NLP works:

 1. For making BERT handle different varieties of downstream tasks, input
    representation definitively represents a set of sentences that are conjoint
    in a single sequence. A sequence cites an input token order to BERT.
 2. The initial token of each sequence is a singular classification token. The
    concluding hidden state after this token is utilized as the average sequence
    presentation for classifying tokens.

You should differentiate between the sentences in two methods. Firstly, you
should disconnect them with a unique token. Secondly, you have to add a learning
model embedding each token which indicates whether it applies to sentence 1 or
sentence 2.

You should denote the input embedding as 5, then the concluding hidden vector of
the unique token as 3, and the concluding hidden vector for the ith input token
as Ti. Then, vector 3 is used for the NSP application.


AUTO-REGRESSIVE LANGUAGE MODELING

When you have auto-encoding models like BERT from transformers or you want to
employ self-supervised learning for functions like sentence classification, then
the application of SSL propositions occurs in the text generation domain.

Auto-regressive models like GPT (Generative Pre-trained Transformers) agreed on
the vintage language modeling task. It will forecast the upcoming word after
reading all the preceding ones. Models like these will respond to the decoder
part of the mask and transformer at the top of the completed sentence before and
after the text.

Now, let us understand more about how these models work by trying to understand
the GPT training framework. The training approach will have two phases:

Unsupervised pre-training phase

This is the first stage. It will help you learn a powerful language model on a
huge amount of text. When you have an unsupervised amount of tokens U = {u1, . .
. . . . . . un}, you can use standard language modeling objectives for
maximizing the following probability:



In the above string, k is the measurement of the context window, and P is the
dependent probability modeled with the help of neural networks with parameters.
These variables are taught with the help of stochastic gradient descent.



You are training a complex transformer decoder for the self-supervised learning
model, which will also act as an alternative to the transformer. It applies a
complex operation over the input context tokens following position-wise
feed-forward layers for producing an output distribution over the targetted
tokens:



In the above equation, U = ( u-k, . . . . . . . , u -1) is the context vector of
tokens, We are the token embedding matrix, Wp is the position embedding matrix,
and n is the total number of layers. This attention where every token can visit
the context to h0 will bring the self-supervised approach into the picture.

Supervised fine-tuning

In this step, you should think of a labeled dataset C, where every instance
contains a set of input tokens,
x1, . . . . . , xm along with a label y. You will pass the inputs through the
pre-trained model for obtaining the final transformer block parameters Wy for
predicting y:



It will provide us with the objective for maximizing:



When you want to add language modeling for the auxiliary purpose of the
fine-tuning, it will help in understanding that improving the generality of the
supervised learning model and increasing convergence is the best choice. You
should optimize the below objective with a weight:



On the whole, the only extra variables you need when fine-tuning are Wy, and
embeddings for delimiter tokens. The transformer architecture and training
objectives are the input transformations for fine-tuning various tasks.

You must convert all the structured inputs into sequences of tokens that need to
be processed by the pre-trained model and after that, we should process the
linear + softmax layer. For various tasks, various types of processing are
needed like Textual Entailment. There are various iterations required when you
want to have an improvement over the original GPT model. It will also help you
in understanding how you can use it for your requirements.


WRAPPING UP

When you are using the above techniques like transfer learning and
self-supervised learning, you will be able to train the deep learning models
even when there aren’t enough resources. It will provide us with a way to train
deep learning models that are not task-specific but can support multiple tasks
with the help of fine-tuning.

Self-supervised learning has highly helped in the development of AI systems,
which can learn with less help. With GPT-3 and BERT you can see that SSL is
easily used in natural language processing. This network aims to learn using
good representations from unlabeled data.

It will highly reduce the dependency on huge amounts of data similar to the
supervised learning model. At present, self-supervised learning is still a
growing technology and developers are massively being hired to make the process
smoother and efficient.


AUTHOR


 * TURING
   
   Author is a seasoned writer with a reputation for crafting highly engaging,
   well-researched, and useful content that is widely read by many of today's
   skilled programmers and developers.


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FREQUENTLY ASKED QUESTIONS

Does NLP use supervised learning?

Machine learning for natural language processing and text analysis requires a
set of statistical methods which will help them with the identification of
speech, sentiments, entities, and other emotional parts of the text. The
technique that is used for expressing it as a model which is then applied to
other texts is known as supervised machine learning.

What is a self-supervised learning example?

One of the well-known examples of a self-supervised learning model is speech
recognition. For example, the application developed by Facebook wav2vec runs on
the self-supervised learning model. It performs speech recognition with the help
of two deep convolutional neural networks which are built on one another.

Are language models self-supervised?

Language models are trained using a self-supervised model which are having tasks
over huge amounts of unlabeled text. For example, when you take the masked
language task, some token fractions in the original text will be masked
randomly, and the language model will attempt in predicting the original text.

What is the use of self-supervised learning?

The self-supervised learning model is a representation where a supervised task
is created with the help of unlabeled data. It is majorly used for reducing the
data labeling cost and leveraging the unlabelled data pool.

What is self-supervised vs unsupervised learning?

Both self-supervised and unsupervised learning models are similar. The major
difference among them is that the self-supervised learning model aims to tackle
tasks that are traditionally done by supervised learning models.

What are the two types of supervised learning models?

The supervised learning models are of two types: regression and classification.
Classification separates the data, whereas the regression fits the data in the
required space.

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