15. Natural Language Processing: Applications¶
We have seen how to represent text tokens and train their representations in Section 14. Such pretrained text representations can be fed to various models for different downstream natural language processing tasks.
This book does not intend to cover natural language processing applications in a comprehensive manner. Our focus is on how to apply (deep) representation learning of languages to addressing natural language processing problems. Nonetheless, we have already discussed several natural language processing applications without pretraining in earlier chapters, just for explaining deep learning architectures. For instance, in Section 8, we have relied on RNNs to design language models to generate novella-like text. In Section 9 and Section 10, we have also designed models based on RNNs and attention mechanisms for machine translation. Given pretrained text representations, in this chapter, we will consider two more downstream natural language processing tasks: sentiment analysis and natural language inference. These are popular and representative natural language processing applications: the former analyzes single text and the latter analyzes relationships of text pairs.
As depicted in Fig. 15.1, this chapter focuses on describing the basic ideas of designing natural language processing models using different types of deep learning architectures, such as MLPs, CNNs, RNNs, and attention. Though it is possible to combine any pretrained text representations with any architecture for either downstream natural language processing task in Fig. 15.1, we select a few representative combinations. Specifically, we will explore popular architectures based on RNNs and CNNs for sentiment analysis. For natural language inference, we choose attention and MLPs to demonstrate how to analyze text pairs. In the end, we introduce how to fine-tune a pretrained BERT model for a wide range of natural language processing applications, such as on a sequence level (single text classification and text pair classification) and a token level (text tagging and question answering). As a concrete empirical case, we will fine-tune BERT for natural language processing.
As we have introduced in Section 14.8, BERT requires minimal architecture changes for a wide range of natural language processing applications. However, this benefit comes at the cost of fine-tuning a huge number of BERT parameters for the downstream applications. When space or time is limited, those crafted models based on MLPs, CNNs, RNNs, and attention are more feasible. In the following, we start by the sentiment analysis application and illustrate the model design based on RNNs and CNNs, respectively.
- 15.1. Sentiment Analysis and the Dataset
- 15.2. Sentiment Analysis: Using Recurrent Neural Networks
- 15.3. Sentiment Analysis: Using Convolutional Neural Networks
- 15.4. Natural Language Inference and the Dataset
- 15.5. Natural Language Inference: Using Attention
- 15.6. Fine-Tuning BERT for Sequence-Level and Token-Level Applications
- 15.7. Natural Language Inference: Fine-Tuning BERT