MST5 -- Multilingual Question Answering over Knowledge Graphs
<|reference_start|>MST5 -- Multilingual Question Answering over Knowledge Graphs: Knowledge Graph Question Answering (KGQA) simplifies querying vast amounts of knowledge stored in a graph-based model using natural language. However, the research has largely concentrated on English, putting non-English speakers at a disadvantage. Meanwhile, existing multilingual KGQA systems face challenges in achieving performance comparable to English systems, highlighting the difficulty of generating SPARQL queries from diverse languages. In this research, we propose a simplified approach to enhance multilingual KGQA systems by incorporating linguistic context and entity information directly into the processing pipeline of a language model. Unlike existing methods that rely on separate encoders for integrating auxiliary information, our strategy leverages a single, pretrained multilingual transformer-based language model to manage both the primary input and the auxiliary data. Our methodology significantly improves the language model's ability to accurately convert a natural language query into a relevant SPARQL query. It demonstrates promising results on the most recent QALD datasets, namely QALD-9-Plus and QALD-10. Furthermore, we introduce and evaluate our approach on Chinese and Japanese, thereby expanding the language diversity of the existing datasets.<|reference_end|>
@article{srivastava2024mst5,
    title={MST5 -- Multilingual Question Answering over Knowledge Graphs},
    author={Nikit Srivastava, Mengshi Ma, Daniel Vollmers, Hamada Zahera, Diego
  Moussallem, Axel-Cyrille Ngonga Ngomo},
    journal={arXiv preprint arXiv:2407.06041},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06041},
    primaryClass={cs.CL}
}