loading...
Cover image for Self Supervised Representation Learning in NLP

Self Supervised Representation Learning in NLP

amitness profile image Amit Chaudhary Originally published at amitness.com Updated on ・6 min read

While Computer Vision is making amazing progress on self-supervised learning only in the last few years, self-supervised learning has been a first-class citizen in NLP research for quite a while. Language Models have existed since the 90's even before the phrase "self-supervised learning" was termed. The Word2Vec paper from 2013 popularized this paradigm and the field has rapidly progressed applying these self-supervised methods across many problems.

At the core of these self-supervised methods lies a framing called "pretext task" that allows us to use the data itself to generate labels and use supervised methods to solve unsupervised problems. These are also referred to as "auxiliary task" or "pre-training task". The representations learned by performing this task can be used as a starting point for our downstream supervised tasks.

Pipeline of pre-training and downstream tasks

In this post, I will provide an overview of the various pretext tasks that researchers have designed to learn representations from text corpus without explicit data labeling. The focus of the article will be on the task formulation rather than the architectures implementing them.

Self-Supervised Formulations

1. Center Word Prediction

In this formulation, we take a small chunk of the text of a certain window size and our goal is to predict the center word given the surrounding words.

Interactive example of center word prediction

For example, in the below image, we have a window of size of one and so we have one word each on both sides of the center word. Using these neighboring words, we need to predict the center word.

Relation of center word, window size and context word

This formulation has been used in the famous "Continuous Bag of Words" approach of the Word2Vec paper.

2. Neighbor Word Prediction

In this formulation, we take a span of the text of a certain window size and our goal is to predict the surrounding words given the center word.

Example of Neighbor Word Prediction

This formulation has been implemented in the famous "skip-gram" approach of the Word2Vec paper.

3. Neighbor Sentence Prediction

In this formulation, we take three consecutive sentences and design a task in which given the center sentence, we need to generate the previous sentence and the next sentence. It is similar to the previous skip-gram method but applied to sentences instead of words.

Example of Neighbor Sentence Prediction

This formulation has been used in the Skip-Thought Vectors paper.

4. Auto-regressive Language Modeling

In this formulation, we take large corpus of unlabeled text and setup a task to predict the next word given the previous words. Since we already know what word should come next from the corpus, we don't need manually-annotated labels.

Auto-Regressive Language Modeling

For example, we could setup the task as left-to-right language modeling by predicting next words given the previous words.

Predicting future word from past

We can also formulate this as predicting the previous words given the future words. The direction will be from right to left.

Right-to-left language modeling

This formulation has been used in many papers ranging from n-gram models to neural network models such as Neural Probabilistic Language Model(Bengio et al., 2003) to GPT.

5. Masked Language Modeling

In this formulation, words in a text are randomly masked and the task is to predict them. Compared to the auto-regressive formulation, we can use context from both previous and next words when predicting the masked word.

Masked Language Modeling

This formulation has been used in the BERT, RoBERTa and ALBERT papers. Compared to the auto-regressive formulation, in this task, we predict only a small subset of masked words and so the amount of things learned from each sentence is lower.

6. Next Sentence Prediction

In this formulation, we take two consecutive sentences present in a document and another sentence from a random location in the same document or a different document.

Next Sentence Prediction data preparation

Then, the task is to classify whether two sentences can come one after another or not.

Next Sentence Prediction task

It was used in the BERT paper to improve performance on downstream tasks that requires an understanding of sentence relations such as Natural Language Inference(NLI) and Question Answering. However, later works have questioned its effectiveness.

7. Sentence Order Prediction

In this formulation, we take pairs of consecutive sentences from the document. Another pair is also created where the positions of the two sentences are interchanged.

Sentence Order Prediction Text Sampling

The goal is to classify if a pair of sentences are in the correct order or not.

Example of Sentence Order Prediction

It was used in the ALBERT paper to replace the "Next Sentence Prediction" task.

8. Sentence Permutation

In this formulation, we take a continuous span of text from the corpus and break the sentences present there. Then, the sentences positions are shuffled randomly and the task is to recover the original order of the sentences.

Sentence Permutation Prediction

It has been used in the BART paper as one of the pre-training tasks.

9. Document Rotation

In this formulation, a random token in the document is chosen as the rotation point. Then, the document is rotated such that this token becomes the starting word. The task is to recover the original sentence from this rotated version.

Document Rotation Pre-training

It has been used in the BART paper as one of the pre-training tasks. The intuition is that this will train the model to identify the start of a document.

10. Emoji Prediction

This formulation was used in the DeepMoji paper and exploits the idea that we use emoji to express the emotion of the thing we are tweeting. As shown below, we can use the emoji present in the tweet as the label and formulate a supervised task to predict the emoji when given the text.

Emoji Prediction from Tweets

Authors of DeepMoji used this concept to perform pre-training of a model on 1.2 billion tweets and then fine-tuned it on emotion-related downstream tasks like sentiment analysis, hate speech detection and insult detection.

References

Connect

If you enjoyed this blog post, feel free to connect with me on Twitter where I share new blog posts every week.

Discussion

pic
Editor guide