Multitask Learning For Darpa Lorelei’S Situation Frame Extraction Task

This paper describes a novel approach of multitask learning for an end-to-end optimization technique for document classification. The application motivation comes from the need to extract "Situation Frames (SF)" from a document within the context of DARPA's LORELEI program targeting humanitarian assistance and disaster relief. We show the benefit of our approach for extracting SF: which includes extracting document types and then linking them to entities. We jointly train a hierarchical document classifier and an auto-encoder using a shared word-level bottleneck layers. Our architecture can exploit additional monolingual corpora in addition to labelled data for classification, thus helping it to generalize over a bigger vocabulary. We evaluate these approaches over standard datasets for this task. Our methods show improvements for both document type prediction and entity linking.