Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction

• URL: http://arxiv.org/abs/2305.12258v3
• Date: Sun, 4 Jun 2023 13:29:51 GMT
• Title: Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction
• Authors: Hao Fei, Meishan Zhang, Min Zhang, Tat-Seng Chua
• Abstract summary: Cross-lingual relation extraction (XRE) aggressively leverage the language-consistent structural features from the universal dependency (UD) resource. We investigate an unbiased UD-based XRE transfer by constructing a type of code-mixed UD forest. With such forest features, the gaps of UD-based XRE between the training and predicting phases can be effectively closed.
• Score: 92.84968716013783
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Latest efforts on cross-lingual relation extraction (XRE) aggressively leverage the language-consistent structural features from the universal dependency (UD) resource, while they may largely suffer from biased transfer (e.g., either target-biased or source-biased) due to the inevitable linguistic disparity between languages. In this work, we investigate an unbiased UD-based XRE transfer by constructing a type of code-mixed UD forest. We first translate the sentence of the source language to the parallel target-side language, for both of which we parse the UD tree respectively. Then, we merge the source-/target-side UD structures as a unified code-mixed UD forest. With such forest features, the gaps of UD-based XRE between the training and predicting phases can be effectively closed. We conduct experiments on the ACE XRE benchmark datasets, where the results demonstrate that the proposed code-mixed UD forests help unbiased UD-based XRE transfer, with which we achieve significant XRE performance gains.

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