Cross-lingual open information extraction aims to extract structured
information from raw text across multiple languages. Previous work uses a
shared cross-lingual pre-trained model to handle the different languages but
underuses the potential of the language-specific representation. In this paper,
we propose an effective multi-stage tuning framework called MT4CrossIE,
designed for enhancing cross-lingual open information extraction by injecting
language-specific knowledge into the shared model. Specifically, the
cross-lingual pre-trained model is first tuned in a shared semantic space
(e.g., embedding matrix) in the fixed encoder and then other components are
optimized in the second stage. After enough training, we freeze the pre-trained
model and tune the multiple extra low-rank language-specific modules using
mixture-of-LoRAs for model-based cross-lingual transfer. In addition, we
leverage two-stage prompting to encourage the large language model (LLM) to
annotate the multi-lingual raw data for data-based cross-lingual transfer. The
model is trained with multi-lingual objectives on our proposed dataset
OpenIE4++ by combing the model-based and data-based transfer techniques.
Experimental results on various benchmarks emphasize the importance of
aggregating multiple plug-in-and-play language-specific modules and demonstrate
the effectiveness of MT4CrossIE in cross-lingual
OIE\footnote{https://github.com/CSJianYang/Multilingual-Multimodal-NLP}.

我们提出了一种名为 MT4CrossIE 的有效的多阶段调整框架，通过将语言特定知识注入共享模型中，增强跨语言开放信息提取，实验证明了聚合多个插拔式语言特定模块的重要性，以及 MT4CrossIE 在跨语言 OIE 中的有效性。