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.

Recently, the credibility of information on the Web has become an important issue. In addition to telling about content of source documents, indicating how to interpret the content, especially showing interpretation of the relation between statements appeared to contradict each other, is important for helping a user judge the credibility of information. In this paper, we will describe the purpose and the way in the construction of a text summarization corpus. Our purpose in the construction of the corpus includes the following three points; to collect Web documents relevant to several query sentences, to prepare gold standard data to evaluate smaller sub-processes in the extraction process and the summary generation process, to investigate the summaries made by human summarizers. The constructed corpus contains six query sentences, 24 manually-constructed summaries, and 24 collections of source Web documents. We also investigated how the descriptions of interpretation, which help a user judge the credibility of other descriptions in the summary, appear in the corpus. As a result, we confirmed that showing interpretation on conflicts is important for helping a user judge the credibility of information.