A Knowledge-Guided Framework for Frame Identification

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A Knowledge-Guided Framework for Frame Identification
Title:
A Knowledge-Guided Framework for Frame Identification
Other Titles:
ACL
Keywords:
Publication Date:
01 August 2021
Citation:
A Knowledge-Guided Framework for Frame Identification, Xuefeng Su, Ru Li, Xiaoli Li, Jeff Z. Pan, Hu Zhang, Qinghua Chai and Xiaoqi Han, Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, August 2021, DOI: 10.18653/v1/2021.acl-long.407, pages 5230--5240
Abstract:
Frame Identification (FI) is a fundamental and challenging task in frame semantic parsing. The task aims to find the exact frame evoked by a target word in a given sentence. It is generally regarded as a classification task in existing work, where frames are treated as discrete labels or represented using onehot embeddings. However, the valuable knowledge about frames is neglected. In this paper, we propose a Knowledge-Guided Frame Identification framework (KGFI) that integrates three types frame knowledge, including frame definitions, frame elements and frame-to-frame relations, to learn better frame representation, which guides the KGFI to jointly map target words and frames into the same embedding space and subsequently identify the best frame by calculating the dot-product similarity scores between the target word embedding and all of the frame embeddings. The extensive experimental results demonstrate KGFI significantly outperforms the state-of-the-art methods on two benchmark datasets.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research is supported by core funding from: Institute for Infocomm Research
Grant Reference no. :
Description:
ISSN:
Nil