@inproceedings{razzhigaev-etal-2023-system,
    title = "A System for Answering Simple Questions in Multiple Languages",
    author = "Razzhigaev, Anton  and
      Salnikov, Mikhail  and
      Malykh, Valentin  and
      Braslavski, Pavel  and
      Panchenko, Alexander",
    editor = "Bollegala, Danushka  and
      Huang, Ruihong  and
      Ritter, Alan",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-demo.51",
    doi = "10.18653/v1/2023.acl-demo.51",
    pages = "524--537",
    abstract = "Our research focuses on the most prevalent type of queries{---} simple questions {---}exemplified by questions like {``}What is the capital of France?{''}. These questions reference an entity such as {``}France{''}, which is directly connected (one hop) to the answer entity {``}Paris{''} in the underlying knowledge graph (KG). We propose a multilingual Knowledge Graph Question Answering (KGQA) technique that orders potential responses based on the distance between the question{'}s text embeddings and the answer{'}s graph embeddings. A system incorporating this novel method is also described in our work. Through comprehensive experimentation using various English and multilingual datasets and two KGs {---} Freebase and Wikidata {---} we illustrate the comparative advantage of the proposed method across diverse KG embeddings and languages. This edge is apparent even against robust baseline systems, including seq2seq QA models, search-based solutions and intricate rule-based pipelines. Interestingly, our research underscores that even advanced AI systems like ChatGPT encounter difficulties when tasked with answering simple questions. This finding emphasizes the relevance and effectiveness of our approach, which consistently outperforms such systems. We are making the source code and trained models from our study publicly accessible to promote further advancements in multilingual KGQA.",
}

Our research focuses on the most prevalent type of queries{---} simple questions {---}exemplified by questions like {``}What is the capital of France?{''}. These questions reference an entity such as {``}France{''}, which is directly connected (one hop) to the answer entity {``}Paris{''} in the underlying knowledge graph (KG). We propose a multilingual Knowledge Graph Question Answering (KGQA) technique that orders potential responses based on the distance between the question{'}s text embeddings and the answer{'}s graph embeddings. A system incorporating this novel method is also described in our work. Through comprehensive experimentation using various English and multilingual datasets and two KGs {---} Freebase and Wikidata {---} we illustrate the comparative advantage of the proposed method across diverse KG embeddings and languages. This edge is apparent even against robust baseline systems, including seq2seq QA models, search-based solutions and intricate rule-based pipelines. Interestingly, our research underscores that even advanced AI systems like ChatGPT encounter difficulties when tasked with answering simple questions. This finding emphasizes the relevance and effectiveness of our approach, which consistently outperforms such systems. We are making the source code and trained models from our study publicly accessible to promote further advancements in multilingual KGQA.
Razzhigaev, Anton Salnikov, Mikhail Malykh, Valentin Braslavski, Pavel Panchenko, Alex er
A System for Answering Simple Questions in Multiple Languages