@inproceedings{unlu-menevse-etal-2024-dealing,
    title = "Dealing with Data Scarcity in Spoken Question Answering",
    author = {{\"U}nl{\"u} Menev{\c{s}}e, Merve  and
      Manav, Yusufcan  and
      Arisoy, Ebru  and
      {\"O}zg{\"u}r, Arzucan},
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.397",
    pages = "4449--4455",
    abstract = "This paper focuses on dealing with data scarcity in spoken question answering (QA) using automatic question-answer generation and a carefully selected fine-tuning strategy that leverages limited annotated data (paragraphs and question-answer pairs). Spoken QA is a challenging task due to using spoken documents, i.e., erroneous automatic speech recognition (ASR) transcriptions, and the scarcity of spoken QA data. We propose a framework for utilizing limited annotated data effectively to improve spoken QA performance. To deal with data scarcity, we train a question-answer generation model with annotated data and then produce large amounts of question-answer pairs from unannotated data (paragraphs). Our experiments demonstrate that incorporating limited annotated data and the automatically generated data through a carefully selected fine-tuning strategy leads to 5.5{\%} relative F1 gain over the model trained only with annotated data. Moreover, the proposed framework is also effective in high ASR errors.",
}

This paper focuses on dealing with data scarcity in spoken question answering (QA) using automatic question-answer generation and a carefully selected fine-tuning strategy that leverages limited annotated data (paragraphs and question-answer pairs). Spoken QA is a challenging task due to using spoken documents, i.e., erroneous automatic speech recognition (ASR) transcriptions, and the scarcity of spoken QA data. We propose a framework for utilizing limited annotated data effectively to improve spoken QA performance. To deal with data scarcity, we train a question-answer generation model with annotated data and then produce large amounts of question-answer pairs from unannotated data (paragraphs). Our experiments demonstrate that incorporating limited annotated data and the automatically generated data through a carefully selected fine-tuning strategy leads to 5.5{\%} relative F1 gain over the model trained only with annotated data. Moreover, the proposed framework is also effective in high ASR errors.
{\"U}nl{\"u} Menev{\c{s}}e, Merve Manav, Yusufcan Arisoy, Ebru {\"O}zg{\"u}r, Arzucan
Dealing with Data Scarcity in Spoken Question Answering