Iterative Document-level Information Extraction via Imitation Learning
Yunmo Chen, William Gantt, Weiwei Gu, Tongfei Chen, Aaron Steven White, Benjamin Van Durme · 2023
We present a novel iterative extraction model, ITERX, for extracting complex relations, or templates, i.e., #-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. 1