FineText: Text Classification via Attention-based Language Model Fine-tuning
This provides a more data-efficient and generalizable solution for customers needing text classification, though it is incremental as it builds on existing transfer learning methods.
The paper tackled the problem of requiring large labeled datasets and long training times for NLP tasks by fine-tuning a pre-trained language model with an attention-based algorithm, achieving state-of-the-art performance on six benchmark datasets.
Training deep neural networks from scratch on natural language processing (NLP) tasks requires significant amount of manually labeled text corpus and substantial time to converge, which usually cannot be satisfied by the customers. In this paper, we aim to develop an effective transfer learning algorithm by fine-tuning a pre-trained language model. The goal is to provide expressive and convenient-to-use feature extractors for downstream NLP tasks, and achieve improvement in terms of accuracy, data efficiency, and generalization to new domains. Therefore, we propose an attention-based fine-tuning algorithm that automatically selects relevant contextualized features from the pre-trained language model and uses those features on downstream text classification tasks. We test our methods on six widely-used benchmarking datasets, and achieve new state-of-the-art performance on all of them. Moreover, we then introduce an alternative multi-task learning approach, which is an end-to-end algorithm given the pre-trained model. By doing multi-task learning, one can largely reduce the total training time by trading off some classification accuracy.