MST5 -- Multilingual Question Answering over Knowledge Graphs
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913421226672128 |
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| author | Srivastava, Nikit Ma, Mengshi Vollmers, Daniel Zahera, Hamada Moussallem, Diego Ngomo, Axel-Cyrille Ngonga |
| author_facet | Srivastava, Nikit Ma, Mengshi Vollmers, Daniel Zahera, Hamada Moussallem, Diego Ngomo, Axel-Cyrille Ngonga |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_06041 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MST5 -- Multilingual Question Answering over Knowledge Graphs Srivastava, Nikit Ma, Mengshi Vollmers, Daniel Zahera, Hamada Moussallem, Diego Ngomo, Axel-Cyrille Ngonga Computation and Language 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. |
| title | MST5 -- Multilingual Question Answering over Knowledge Graphs |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2407.06041 |