3 research outputs found
Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction
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