We propose a novel method for hierarchical entity classification that embraces ontological structure at both training and during prediction. At training, our novel multi-level learning-to-rank loss compares positive types against negative siblings according to the type tree. During prediction, we define a coarseto-fine decoder that restricts viable candidates at each level of the ontology based on already predicted parent type(s). We achieve stateof-the-art across multiple datasets, particularly with respect to strict accuracy. 1

We present LOME, a system for performing multilingual information extraction. Given a text document as input, our core system identifies spans of textual entity and event mentions with a FrameNet (Baker et al., 1998) parser. It subsequently performs coreference resolution, fine-grained entity typing, and temporal relation prediction between events. By doing so, the system constructs an event and entity focused knowledge graph. We can further apply third-party modules for other types of annotation, like relation extraction. Our (multilingual) first-party modules either outperform or are competitive with the (monolingual) state-of-the-art. We achieve this through the use of multilingual encoders like XLM-R (Conneau et al., 2020) and leveraging multilingual training data. LOME is available as a Docker container on Docker Hub. In addition, a lightweight version of the system is accessible as a web demo. * Equal Contribution 1 Information on using the Docker container, web demo, and demo video at https://nlp.jhu.edu/demos.

We ask whether text understanding has progressed to where we may extract event information through incremental refinement of bleached statements derived from annotation manuals. Such a capability would allow for the trivial construction and extension of an extraction framework by intended end-users through declarations such as, Some person was born in some location at some time. We introduce an example of a model that employs such statements, with experiments illustrating we can extract events under closed ontologies and generalize to unseen event types simply by reading new definitions.

We recognize the task of event argument linking in documents as similar to that of intent slot resolution in dialogue, providing a Transformer-based model that extends from a recently proposed solution to resolve references to slots. The approach allows for joint consideration of argument candidates given a detected event, which we illustrate leads to state-of-the-art performance in multi-sentence argument linking. 1

We propose a novel approach to event extraction that supplies models with bleached statements: machine-readable natural language sentences that are based on annotation guidelines and that describe generic occurrences of events. We introduce a model that incrementally replaces the bleached arguments in a statement with responses obtained by querying text with the statement itself. Experimental results demonstrate that our model is able to extract events under closed ontologies and can generalize to unseen event types simply by reading new bleached statements.