Autoregressive Structure Prediction with Language Models


In recent years, NLP has moved towards the application of language models to a more diverse set of tasks. However, applying language models to structured prediction, e.g., predicting parse trees, taggings, and coreference chains, is not straightforward. Prior work on language model-based structured prediction typically flattens the target structure into a string to easily fit it into the language modeling framework. Such flattening limits the accessibility of structural information and can lead to inferior performance compared to approaches that overtly model the structure. In this work, we propose to construct a conditional language model over sequences of structure-building actions, rather than over strings in a way that makes it easier for the model to pick up on intra-structure dependencies. Our method sets the new state of the art on named entity recognition, end-to-end relation extraction, and coreference resolution.

Findings of the Association for Computational Linguistics: EMNL 2022