Keywords: language models, few-shot learning, transfer learning, structured prediction, generative modeling, sequence to sequence, multi-task learning
Abstract: We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks including joint entity and relation extraction, nested named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, and dialogue state tracking. Instead of tackling the problem by training task-specific discriminative classifiers, we frame it as a translation task between augmented natural languages, from which the task-relevant information can be easily extracted. Our approach can match or outperform task-specific models on all tasks, and in particular achieves new state-of-the-art results on joint entity and relation extraction (CoNLL04, ADE, NYT, and ACE2005 datasets), relation classification (FewRel and TACRED), and semantic role labeling (CoNLL-2005 and CoNLL-2012). We accomplish this while using the same architecture and hyperparameters for all tasks, and even when training a single model to solve all tasks at the same time (multi-task learning). Finally, we show that our framework can also significantly improve the performance in a low-resource regime, thanks to better use of label semantics.
One-sentence Summary: We propose a unified text-to-text approach to handle a variety of structured prediction tasks in a single model, allowing seamless multi-task training and providing extra benefits on low-resource scenarios.
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Code: [![github](/images/github_icon.svg) amazon-research/tanl](https://github.com/amazon-research/tanl)
Data: [FewRel](https://paperswithcode.com/dataset/fewrel), [TACRED](https://paperswithcode.com/dataset/tacred)