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 MT4CrossOIE, 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 multilingual raw data for data-based cross-lingual transfer. The model is trained with multilingual 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 languagespecific modules and demonstrate the effectiveness of MT4CrossOIE in cross-lingual OIE.
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- arXiv: https://arxiv.org/abs/2308.06552
@article{wang2023mt4crossoie,
title = {MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction},
author = {Wang, Zixiang and Chai, Linzheng and Yang, Jian and Bai, Jiaqi and Yin, Yuwei and
Liu, Jiaheng and Guo, Hongcheng and Li, Tongliang and Yang, Liqun and
Hebboul, Zine el-abidine and Li, Zhoujun},
journal = {arXiv preprint arXiv:2308.06552},
year = {2023},
}