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A common self-supervised learning strategy for training encoders involves formulating classification tasks. These tasks are designed by creating classification challenges directly from unlabeled text, providing the necessary supervision signals without manual annotation. There are numerous approaches to designing these self-supervised classification tasks.

Self-Supervised Classification Tasks for Encoder Training

A simple method for designing a self-supervised classification task to train an encoder is Next Sentence Prediction (NSP), as presented in the original BERT paper. This approach is built on the assumption that a good text encoder should effectively capture the relationship between two sentences. To model this, NSP uses the output of encoding two consecutive sentences, $$\mathrm{Sent}_{A}$$ and $$\mathrm{Sent}_{B}$$, to determine whether $$\mathrm{Sent}_{B}$$ is indeed the next sentence following $$\mathrm{Sent}_{A}$$. For example, if $$\mathrm{Sent}_{A}$$ is 'It is raining .' and $$\mathrm{Sent}_{B}$$ is 'I need an umbrella .', the model is tasked with recognizing this sequential relationship.

Next Sentence Prediction (NSP)

A method for training Transformer encoders as classifiers involves applying a distinct supervision signal to the output corresponding to each token in a sequence. In this setup, the model learns by making a classification decision for every individual token, such as identifying if a token has been altered. This per-token objective, exemplified by the ELECTRA model, contrasts with approaches that generate a single classification for an entire sequence.

Per-Token Classification for Encoder Training

Imagine you need to train a model to understand the nuances of a language using a vast library of unlabeled books. Your challenge is to create a learning task for the model without manually creating any labels. Propose a novel **binary classification task** that can be automatically generated from the raw text. Your proposal must describe:
1. The process for creating 'positive' and 'negative' examples from the text.
2. What the model will predict for any given example.
3. A brief justification for why successfully performing this task would lead to a better understanding of language.

Designing a Self-Supervised Text Classification Task

A researcher aims to pre-train a text encoder on a large corpus of unlabeled articles. They propose the following self-supervised classification task: For each training instance, a paragraph is extracted. With 50% probability, the sentences within that paragraph are randomly reordered. The model's task is to predict a binary label: 'Original Order' or 'Shuffled Order'. Which statement best evaluates the potential effectiveness of this task for its intended purpose?

A key aspect of training text encoders with self-supervision is designing a classification task that forces the model to learn a useful property of language. Match each proposed self-supervised classification task with the primary linguistic property it is designed to teach the model.

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