Improving Language Understanding by Generative Pre-Training (GPT): Difference between revisions

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Introduction

Learning from raw text is essential to reduce the dependency on supervised learning in natural language processing (NLP). While most deep learning models require annotated data, the use of linguistic information from unlabeled data provides an alternative to gather more annotation, which can be costly and time-consuming. Pre-trained word embeddings have shown promising results in enhancing the performance of various NLP tasks. However, leveraging more than word-level information from unlabeled data is challenging due to the lack of consensus on optimization objectives and the most effective way to transfer learned representations to the target task. This paper proposes a semi-supervised approach for language understanding tasks using unsupervised pre-training and supervised fine-tuning, which does not require the target tasks to be in the same domain as the unlabeled corpus. The approach employs the Transformer model architecture and task-specific input adaptations derived from traversal-style approaches. Experiments show that the model outperforms discriminatively trained models, achieving significant improvements in several language understanding tasks. The pre-trained model also acquires useful linguistic knowledge for downstream tasks, even with zero-shot settings.

Related Work

Semi-supervised learning for NLP

Semi-supervised learning has attracted significant interest in natural language processing, with applications in tasks such as sequence labeling and text classification. Early approaches computed word or phrase-level statistics using unlabeled data, which were then used in supervised models. However, recent research has shown that word embeddings trained on unlabeled corpora can improve performance on various tasks. More recent approaches investigate learning and utilizing higher-level semantics from unlabeled data, such as phrase or sentence-level embeddings, to encode text into suitable vector representations for various target tasks.

Unsupervised pre-training

Unsupervised pre-training involves finding a good initialization point for a model, rather than modifying the supervised learning objective. It has been used to improve generalization in deep neural networks for various tasks, such as image classification, speech recognition, and machine translation. Some researchers have pre-trained a neural network using a language modeling objective and fine-tuned it on a target task with supervision to improve text classification. However, this method is limited by the use of LSTM models, which restricts their prediction ability to a short range. In contrast, using transformer networks allows the capture of long-range linguistic structure, as shown in experiments. Moreover, this approach requires minimal changes to the model architecture during transfer, unlike other approaches that require a substantial amount of new parameters for each separate target task.

Auxiliary training objectives

In semi-supervised learning, an alternative approach is to add auxiliary unsupervised training objectives. Collobert and Weston used auxiliary NLP tasks to improve semantic role labeling. Rei added an auxiliary language modeling objective to their target task objective to improve sequence labeling tasks. Similarly, our experiments also use an auxiliary objective to improve target tasks, but unsupervised pre-training is capable of learning relevant linguistic aspects.

Framework

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-===Introduction===
+{{Needs Links}}
-In June 2018, OpenAI introduced GPT-1, a language model that combined unsupervised pre-training with the transformer architecture to achieve significant progress in natural language understanding. The team fine-tuned the model for specific tasks and found that pre-training helped it perform well on various NLP tasks with minimal fine-tuning. GPT-1 used the BooksCorpus dataset and self-attention in the transformer's decoder with 117 million parameters, paving the way for future models with more parameters and larger datasets to enhance its potential further. One noteworthy feature of GPT-1 was its ability to perform zero-shot tasks in natural language processing, such as question-answering and sentiment analysis, thanks to pre-training. Zero-shot learning enables the model to understand a task based on instructions and a few examples without having seen previous examples of that task. GPT-1 was a crucial building block in the development of a language model with general language-based capabilities.
+==Introduction==
+Learning from raw text is essential to reduce the dependency on supervised learning in natural language processing (NLP). While most deep learning models require annotated data, the use of linguistic information from unlabeled data provides an alternative to gather more annotation, which can be costly and time-consuming. Pre-trained word embeddings have shown promising results in enhancing the performance of various NLP tasks. However, leveraging more than word-level information from unlabeled data is challenging due to the lack of consensus on optimization objectives and the most effective way to transfer learned representations to the target task. This paper proposes a semi-supervised approach for language understanding tasks using unsupervised pre-training and supervised fine-tuning, which does not require the target tasks to be in the same domain as the unlabeled corpus. The approach employs the Transformer model architecture and task-specific input adaptations derived from traversal-style approaches. Experiments show that the model outperforms discriminatively trained models, achieving significant improvements in several language understanding tasks. The pre-trained model also acquires useful linguistic knowledge for downstream tasks, even with zero-shot settings.
-===Related Work===
+==Related Work==
-In the realm of natural language processing, our research is part of the semi-supervised learning paradigm, which has gained significant interest and has been applied to a range of tasks such as sequence labeling, text classification, and more. Early approaches computed word-level or phrase-level statistics from unlabeled data to use as features in a supervised model. Later, researchers found the benefits of using word embeddings trained on unlabeled corpora to improve performance on various tasks, though mainly at the word-level. Our work aims to capture higher-level semantics and incorporates recent approaches that use phrase-level or sentence-level embeddings to encode text into suitable vector representations for various target tasks.
+===Semi-supervised learning for NLP===
+Semi-supervised learning has attracted significant interest in natural language processing, with applications in tasks such as sequence labeling and text classification. Early approaches computed word or phrase-level statistics using unlabeled data, which were then used in supervised models. However, recent research has shown that word embeddings trained on unlabeled corpora can improve performance on various tasks. More recent approaches investigate learning and utilizing higher-level semantics from unlabeled data, such as phrase or sentence-level embeddings, to encode text into suitable vector representations for various target tasks.
-Unsupervised pre-training is a special case of semi-supervised learning that aims to find a good initialization point instead of modifying the supervised learning objective. Pre-training acts as a regularization scheme, enabling better generalization in deep neural networks. Recent research has used the method to help train deep neural networks on various tasks such as image classification, speech recognition, entity disambiguation, and machine translation. In our work, we pre-trained a neural network using a language modeling objective and then fine-tuned it on a target task with supervision. Our choice of transformer networks allowed us to capture longer-range linguistic structure, which was demonstrated in our experiments. Our model was effective on a range of tasks, including natural language inference, paraphrase detection, and story completion, requiring minimal changes to the model architecture during transfer.
+===Unsupervised pre-training===
+Unsupervised pre-training involves finding a good initialization point for a model, rather than modifying the supervised learning objective. It has been used to improve generalization in deep neural networks for various tasks, such as image classification, speech recognition, and machine translation. Some researchers have pre-trained a neural network using a language modeling objective and fine-tuned it on a target task with supervision to improve text classification. However, this method is limited by the use of LSTM models, which restricts their prediction ability to a short range. In contrast, using transformer networks allows the capture of long-range linguistic structure, as shown in experiments. Moreover, this approach requires minimal changes to the model architecture during transfer, unlike other approaches that require a substantial amount of new parameters for each separate target task.
-Auxiliary training objectives are an alternative form of semi-supervised learning that involves adding auxiliary unsupervised training objectives to improve performance on target tasks. Our experiments also use an auxiliary objective, and we show that unsupervised pre-training learns several linguistic aspects relevant to target tasks. However, our approach differs from other approaches that use hidden representations from a pre-trained language or machine translation model as auxiliary features while training a supervised model on the target task, which involves a substantial amount of new parameters for each separate target task.
+===Auxiliary training objectives===
+In semi-supervised learning, an alternative approach is to add auxiliary unsupervised training objectives. Collobert and Weston used auxiliary NLP tasks to improve semantic role labeling. Rei added an auxiliary language modeling objective to their target task objective to improve sequence labeling tasks. Similarly, our experiments also use an auxiliary objective to improve target tasks, but unsupervised pre-training is capable of learning relevant linguistic aspects.
+==Framework==