Cross-lingual Annotation Projection in Legal Texts

Proceedings of the 28th International Conference on Computational Linguistics, pages 77–87 Barcelona, Spain (Online), December 8-13, 2020 77 MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing Tao Zhang1, Congying Xia1∗, Chun-Ta Lu2, Philip S. Yu1 1University of Illinois at Chicago, Chicago, IL, USA; 2Google Research, Mountain view, CA, USA {tzhang90,cxia8,psyu}@uic.edu; chunta@google.com Abstract Named entity typing (NET) is a classification task of assigning an entity mention in the context with given semantic types. However, with the growing size and granularity of the entity types, few previous researches concern with newly emerged entity types. In this paper, we propose MZET, a novel memory augmented FNET (Fine-grained NET) model, to tackle the unseen types in a zero-shot manner. MZET incorporates character-level, word-level, and contextural-level information to learn the entity mention representation. Besides, MZET considers the semantic meaning and the hierarchical structure into the entity type representation. Finally, through the memory component which models the relationship between the entity mention and the entity type, MZET transfers the knowledge from seen entity types to the zero-shot ones. Extensive experiments on three public datasets show the superior performance obtained by MZET, which surpasses the state-of-the-art FNET neural network models with up to 8% gain in Micro-F1 and Macro-F1 score. 1 Introduction Named entity typing (NET) is the task of inferring semantic types for the given named entity mentions in utterances. For instance, given an entity mention “John” in the utterance “John plays piano on the stage”. The goal for NET is to infer that “John” is a pianist or a musician, and a person. Standard NET approaches (Chinchor and Robinson, 1997; Tjong Kim Sang and De Meulder, 2003; Doddington et al., 2004) only consider a tiny set of coarse-grained types, and discard fine-grained types with a different level of granularity. In recent years, fine-grained named entity typing (FNET) (Ling and Weld, 2012; Nakashole et al., 2013; Del Corro et al., 2015; Ren et al., 2016; Abhishek et al., 2017; Zhou et al., 2018) continues to draw researchers’ attention, because it can provide additional information that benefits a lot of downstream tasks like relation extraction (Liu et al., 2014), entity linking (Stern et al., 2012), and question answering (Han et al., 2017). However, with the ever-growing number of entity types especially for fine-grained ones, it is difficult and expensive to collect sufficient annotations per category and retrain the whole model. Therefore, a zero-shot paradigm is welcomed in FNET to handle the increasing number of unseen types. The task we deal with in this paper is named zero-shot fine-grained named entity typing (ZFNET), which is to detect the unseen fine-grained entity types that have no labeled data available. Learning generalizable representations for entity mentions and types is essential for the ZFNET task. Previous works learn these representations either from hand-crafted features (Ma et al., 2016; Yuan and Downey, 2018), or pre-trained word embeddings (Ren et al., 2016). These methods are insufficient and inefficient when challenged by poly-semantic, ambiguity, or even the newly-emerged mentions. The most recent works (Obeidat …

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