Event extraction for low-resource languages such as Vietnamese is limited by the lack of large-scale annotated data. To address this, we propose a weakly supervised framework that constructs a silver corpus via pseudo-labeling. We introduce a cross-document n-ary relation filtering strategy to reduce noise by leveraging consistency across multiple articles describing the same event, and further enhance data diversity with schema-based augmentation. Experiments on the BKEE benchmark show consistent improvements, demonstrating the effectiveness of our approach. Data is available at: https://github.com/Larken1612/VietEE2.

The labor market is experiencing rapid and continual shifts in required skills and competencies, driven by technological advancement and evolving industry structures. Within this dynamic environment, candidates increasingly face challenges in orienting their career development, requiring them to continuously update their knowledge and capabilities to meet contemporary job requirements; this need is particularly necessary for new entrants to the labor market, who must cultivate a comprehensive understanding of current labor-market conditions. To address these issues, this study proposes an enterprise recruitment framework grounded in a career path knowledge graph, capturing occupations, skill requirements, and career transitions using standardized taxonomies enriched with job-posting data. The framework integrates transformer-based embeddings, large language models, and knowledge-graph reasoning to support efficient and reliable CV assessment, CV-JD matching and career guidance.

This paper describes a system developed to summarize multiple answers challenge in the MEDIQA 2021 shared task collocated with the BioNLP 2021 Workshop. We propose an extractive summarization architecture based on several scores and state-of-the-art techniques. We also present our novel prosper-thy-neighbour strategies to improve performance. Our model has been proven to be effective with the best ROUGE-1/ROUGE-L scores, being the shared task runner up by ROUGE-2 F1 score (over 13 participated teams).