  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.
