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.

本文研究了一种基于混合代码 UD 林的无偏 UD 的跨语言关系提取方法，并在 ACE XRE 基准数据集上进行实验，证明了该方法在 XRE 性能上获得显着提升。

构建混合代码通用依赖森林进行无偏跨语言关系提取

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

Relation Extraction (RE) is a crucial task in Information Extraction, which
entails predicting relationships between entities within a given sentence.
However, extending pre-trained RE models to other languages is challenging,
particularly in real-world scenarios where Cross-Lingual Relation Extraction
(XRE) is required. Despite recent advancements in Prompt-Learning, which
involves transferring knowledge from Multilingual Pre-trained Language Models
(PLMs) to diverse downstream tasks, there is limited research on the effective
use of multilingual PLMs with prompts to improve XRE. In this paper, we present
a novel XRE algorithm based on Prompt-Tuning, referred to as Prompt-XRE. To
evaluate its effectiveness, we design and implement several prompt templates,
including hard, soft, and hybrid prompts, and empirically test their
performance on competitive multilingual PLMs, specifically mBART. Our extensive
experiments, conducted on the low-resource ACE05 benchmark across multiple
languages, demonstrate that our Prompt-XRE algorithm significantly outperforms
both vanilla multilingual PLMs and other existing models, achieving
state-of-the-art performance in XRE. To further show the generalization of our
Prompt-XRE on larger data scales, we construct and release a new XRE dataset-
WMT17-EnZh XRE, containing 0.9M English-Chinese pairs extracted from WMT 2017
parallel corpus. Experiments on WMT17-EnZh XRE also show the effectiveness of
our Prompt-XRE against other competitive baselines. The code and newly
constructed dataset are freely available at
https://github.com/HSU-CHIA-MING/Prompt-XRE.

本研究提出了一种基于 Prompt-Tuning 的 XRE 算法，使用多语言 Pre-trained 语言模型，通过硬性提示、软性提示、混合提示等多个提示模板来改进 XRE 的效果，实验表明我们的算法在多语言环境下有显著的效果并且在新数据集上也表现优秀，代码和数据集已开放。