@inproceedings{gowriraj-etal-2023-language,
    title = "Language-Agnostic Transformers and Assessing {C}hat{GPT}-Based Query Rewriting for Multilingual Document-Grounded {QA}",
    author = "Gowriraj, Srinivas  and
      Tiwari, Soham Dinesh  and
      Potnis, Mitali  and
      Bansal, Srijan  and
      Mitamura, Teruko  and
      Nyberg, Eric",
    editor = "Muresan, Smaranda  and
      Chen, Vivian  and
      Casey, Kennington  and
      David, Vandyke  and
      Nina, Dethlefs  and
      Koji, Inoue  and
      Erik, Ekstedt  and
      Stefan, Ultes",
    booktitle = "Proceedings of the Third DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.dialdoc-1.11",
    doi = "10.18653/v1/2023.dialdoc-1.11",
    pages = "101--108",
    abstract = "The DialDoc 2023 shared task has expanded the document-grounded dialogue task to encompass multiple languages, despite having limited annotated data. This paper assesses the effectiveness of both language-agnostic and language-aware paradigms for multilingual pre-trained transformer models in a bi-encoder-based dense passage retriever (DPR), concluding that the language-agnostic approach is superior. Additionally, the study investigates the impact of query rewriting techniques using large language models, such as ChatGPT, on multilingual, document-grounded question-answering systems. The experiments conducted demonstrate that, for the examples examined, query rewriting does not enhance performance compared to the original queries. This failure is due to topic switching in final dialogue turns and irrelevant topics being considered for query rewriting.",
}

The DialDoc 2023 shared task has expanded the document-grounded dialogue task to encompass multiple languages, despite having limited annotated data. This paper assesses the effectiveness of both language-agnostic and language-aware paradigms for multilingual pre-trained transformer models in a bi-encoder-based dense passage retriever (DPR), concluding that the language-agnostic approach is superior. Additionally, the study investigates the impact of query rewriting techniques using large language models, such as ChatGPT, on multilingual, document-grounded question-answering systems. The experiments conducted demonstrate that, for the examples examined, query rewriting does not enhance performance compared to the original queries. This failure is due to topic switching in final dialogue turns and irrelevant topics being considered for query rewriting.
Gowriraj, Srinivas Tiwari, Soham Dinesh Potnis, Mitali Bansal, Srijan Mitamura, Teruko Nyberg, Eric
Language-Agnostic Transformers and Assessing ChatGPT-Based Query Rewriting for Multilingual Document-Grounded QA