Go to ACL ARR 2025 February homepagePoint and Line: Multilingual Mutual Reinforcement Effect Mix Information Extraction Datasets
The Mutual Reinforcement Effect (MRE) represents a promising avenue in information extraction and multitasking research. Nevertheless, its applicability has been constrained due to the exclusive availability of MRE mix datasets in Japanese, thereby limiting comprehensive exploration by the global research community. To address this limitation, we introduce a Multilingual MRE mix dataset (MMM) that encompasses 21 sub-datasets in English, Japanese, and Chinese. In this paper, we also propose a method for dataset translation assisted by Large Language Models (LLMs), which significantly reduces the manual annotation time required for dataset construction by leveraging LLMs to translate the original Japanese datasets. Additionally, we have enriched the dataset by incorporating open-domain Named Entity Recognition (NER) and sentence classification tasks. Utilizing this expanded dataset, we developed a unified input-output framework to train an Open-domain Information Extraction Large Language Model (OIELLM). The OIELLM model demonstrates the capability to effectively process novel MMM datasets, exhibiting significant improvements in performance. Furthermore, we conducted a new ablation study to evaluate the MRE across 21 MMM sub-datasets. The results demonstrated that 76% of the datasets exhibited MRE, reinforcing its robustness. Additionally, we applied the MRE datasets to a knowledgeable verbalizer (KV), and the results confirmed that KV constructed by MRE Mix datasets achieved superior KV performance. This further validates the effectiveness of MRE in enhancing IE subtasks.