Anisotropic span embeddings and the negative impact of higher-order inference for coreference resolution: An empirical analysis
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Date
2024-01-25
Open Access Location
Journal Title
Journal ISSN
Volume Title
Publisher
Cambridge University Press
Rights
(c) 2024 The Author/s
CC BY 4.0
CC BY 4.0
Abstract
Coreference resolution is the task of identifying and clustering mentions that refer to the same entity in a document. Based on state-of-the-art deep learning approaches, end-to-end coreference resolution considers all spans as candidate mentions and tackles mention detection and coreference resolution simultaneously. Recently, researchers have attempted to incorporate document-level context using higher-order inference (HOI) to improve end-to-end coreference resolution. However, HOI methods have been shown to have marginal or even negative impact on coreference resolution. In this paper, we reveal the reasons for the negative impact of HOI coreference resolution. Contextualized representations (e.g., those produced by BERT) for building span embeddings have been shown to be highly anisotropic. We show that HOI actually increases and thus worsens the anisotropy of span embeddings and makes it difficult to distinguish between related but distinct entities (e.g., pilots and flight attendants). Instead of using HOI, we propose two methods, Less-Anisotropic Internal Representations (LAIR) and Data Augmentation with Document Synthesis and Mention Swap (DSMS), to learn less-anisotropic span embeddings for coreference resolution. LAIR uses a linear aggregation of the first layer and the topmost layer of contextualized embeddings. DSMS generates more diversified examples of related but distinct entities by synthesizing documents and by mention swapping. Our experiments show that less-anisotropic span embeddings improve the performance significantly (+2.8 F1 gain on the OntoNotes benchmark) reaching new state-of-the-art performance on the GAP dataset.
Description
Keywords
Coreference resolution, higher-order inference, anisotropic span embeddings, contextualized representations
Citation
Hou F, Wang R, Ng SK, Zhu F, Witbrock M, Cahan SF, Chen L, Jia X. (2024). Anisotropic span embeddings and the negative impact of higher-order inference for coreference resolution: An empirical analysis. Natural Language Engineering. First View.