natural-language-processing-9
4-ary Relation Extraction
3260 papers • 126 benchmarks • 313 datasets
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Most implemented papers
Iterative Document-level Information Extraction via Imitation Learning
Benjamin Van Durme, Yunmo Chen, Aaron Steven White, William Gantt Walden, Weiwei Gu, Tongfei Chen•Tue Oct 11 2022
We present a novel iterative extraction model, IterX, for extracting complex relations, or templates, i.e., N-tuples representing a mapping from named slots to spans of text within a document. 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. Our imitation learning approach casts the problem as a Markov decision process (MDP), and relieves the need to use predefined template orders to train an extractor. It leads to state-of-the-art results on two established benchmarks – 4-ary relation extraction on SciREX and template extraction on MUC-4 – as well as a strong baseline on the new BETTER Granular task.