Towards zero-shot cross-lingual named entity disambiguation

[EN]In cross-Lingual Named Entity Disambiguation (XNED) the task is to link Named Entity mentions in text in some native language to English entities in a knowledge graph. XNED systems usually require training data for each native language, limiting their application for low resource languages with small amounts of training data. Prior work have proposed so-called zero-shot transfer systems which are only trained in English training data, but required native prior probabilities of entities with respect to mentions, which had to be estimated from native training examples, limiting their practical interest. In this work we present a zero-shot XNED architecture where, instead of a single disambiguation model, we have a model for each possible mention string, thus eliminating the need for native prior probabilities. Our system improves over prior work in XNED datasets in Spanish and Chinese by 32 and 27 points, and matches the systems which do require native prior information. We experiment with different multilingual transfer strategies, showing that better results are obtained with a purpose-built multilingual pre-training method compared to state-of-the-art generic multilingual models such as XLM-R. We also discovered, surprisingly, that English is not necessarily the most effective zero-shot training language for XNED into English. For instance, Spanish is more effective when training a zero-shot XNED system that dis-ambiguates Basque mentions with respect to an English knowledge graph.This work has been partially funded by the Basque Government (IXA excellence research group (IT1343-19) and DeepText project), Project BigKnowledge (Ayudas Fundacion BBVA a equipos de investigacion cientifica 2018) and via the IARPA BETTER Program contract 2019-19051600006 (ODNI, IARPA activity). Ander Barrena enjoys a post-doctoral grant ESPDOC18/101 from the UPV/EHU and also acknowledges the support of the NVIDIA Corporation with the donation of a Titan V GPU used for this research. The author thankfully acknowledges the computer resources at CTE-Power9 + V100 and technical support provided by Barcelona Supercomputing Center (RES-IM-2020-1-0020)

Identity and Granularity of Events in Text

In this paper we describe a method to detect event descrip- tions in different news articles and to model the semantics of events and their components using RDF representations. We compare these descriptions to solve a cross-document event coreference task. Our com- ponent approach to event semantics defines identity and granularity of events at different levels. It performs close to state-of-the-art approaches on the cross-document event coreference task, while outperforming other works when assuming similar quality of event detection. We demonstrate how granularity and identity are interconnected and we discuss how se- mantic anomaly could be used to define differences between coreference, subevent and topical relations.Comment: Invited keynote speech by Piek Vossen at Cicling 201