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.

提出了一种新的框架，称为TANL，可用于各种结构预测语言任务，包括实体关系提取，嵌套命名实体识别，关系分类，语义角色标注，事件提取，共指消解和对话状态跟踪。使用相同的架构和超参数解决所有任务，甚至可以训练一个模型来同时解决所有任务（多任务学习），该方法在各项任务上可与或优于特定任务的模型，并在一些任务上实现了最新的最好结果。同时还表明，由于更好地利用标签语义，该框架还可以显着提高低资源制约下的性能。

结构化预测作为增广自然语言之间的翻译