Description-Based Zero-shot Fine-Grained Entity Typing

Abstract

Fine-grained Entity typing (FGET) is the task of assigning a fine-grained type from a hierarchy to entity mentions in the text. As the taxonomy of types evolves continuously, it is desirable for an entity typing system to be able to recognize novel types without additional training. This work proposes a zero-shot entity typing approach that utilizes the type description available from Wikipedia to build a distributed semantic representation of the types. During training, our system learns to align the entity mentions and their corresponding type representations on the known types. At test time, any new type can be incorporated into the system given its Wikipedia descriptions. We evaluate our approach on FIGER, a public benchmark entity tying dataset. Because the existing test set of FIGER covers only a small portion of the fine-grained types, we create a new test set by manually annotating a portion of the noisy training data. Our experiments demonstrate the effectiveness of the proposed method in recognizing novel types that are not present in the training data.

MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing

“…To enable the zero-shot paradigm, previous researches (Ma et al, 2016;Obeidat et al, 2019) introduce a score function f (•) to rate the match of a given entity mention x and an entity type y, where y is the raw type picked from Y seen or Y unseen . The definition for f (•) is:…”

“…Another direction is to construct a shared space for linking the seen and unseen data. These models (Ma et al, 2016;Yuan and Downey, 2018;Obeidat et al, 2019) map the mention and label embedding into a shared latent space, then estimate the closeness score for each mention-label pair. Most of the existing zero-shot FNET methods limit the model's flexibility with considerable auxiliary resources or pre-prepared hand-crafted features.…”

“…These methods are insufficient and inefficient when challenged by poly-semantic, ambiguity, or even the newly-emerged mentions. The most recent works (Obeidat et al, 2019;Zhou et al, 2018) learn more informative but resource-costing representations by assembling the exterior Wikipedia knowledge base.…”

“…With the learned representations for entity mentions and entity types, most of the existing zero-shot FNET methods (Ma et al, 2016;Obeidat et al, 2019) project them into a shared semantic space. The shared space is learned through minimizing the distance between entity mentions and its corresponding seen entity types.…”

Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack

“…However, these models require massive amounts of labeled data for a new domain, hindering the rapid development of new tasks. To address the data-intensiveness problem, domain adaptation approaches (Bapna et al, 2017;Lee and Jha, 2019;Shah et al, 2019;Obeidat et al, 2019;Liu et al, 2020b;He et al, 2020c) have been successfully applied. In this paper, we focus on zero-shot cross-domain transfer learning which leverages knowledge learned in the source domains and adapts the models to the target domain without labeled training samples in the target domain.…”