A Multilingual Dataset and Empirical Validation for the Mutual Reinforcement Effect in Information Extraction

TL;DR

This paper introduces a multilingual dataset and empirical validation for the Mutual Reinforcement Effect in information extraction, demonstrating its consistency across languages and task settings.

Contribution

The paper presents the Multilingual MRE Mix dataset and an LLM-assisted translation framework, enabling systematic empirical validation of MRE across multiple languages.

Findings

76% of MMM sub-datasets show MRE across languages

Proposed framework reduces manual annotation effort

Empirical validation confirms MRE's practical value

Abstract

The Mutual Reinforcement Effect (MRE) describes a phenomenon in information extraction where word-level and sentence-level tasks can mutually improve each other when jointly modeled. While prior work has reported MRE in Japanese, its generality across languages and task settings has not been empirically validated, largely due to the lack of multilingual MRE datasets. To address this limitation, we introduce the Multilingual MRE Mix dataset (MMM), which consists of 21 sub-datasets covering English, Japanese, and Chinese. We propose an LLM-assisted dataset translation and alignment framework that significantly reduces manual annotation effort while preserving the structural requirements of MRE tasks. Building on MMM, we adopt a unified input-output framework to train an open-domain information extraction model and conduct extensive empirical studies, including full fine-tuning ablations and the construction of knowledgeable verbalizers based on MRE-mix data. Experimental results show that 76 percent of the MMM sub-datasets consistently exhibit the Mutual Reinforcement Effect across languages. These findings provide systematic empirical validation of MRE in multilingual settings and demonstrate its practical value for information extraction.