Iterative Document-level Information Extraction via Imitation Learning (2023.eacl-main)
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| Challenge: | Documents may feature zero or more instances of a template of any given type, and the task of template extraction entails identifying the templates in a document and extracting each template’s slot values. |
| Approach: | They propose to use iterative extraction to extract complex relations, i.e., N-tuples representing a mapping from named slots to spans of text within a document. |
| Outcome: | The proposed model leads to state-of-the-art results on two established benchmarks and a strong baseline on the new BETTER Granular task. |
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