WolBanking77：沃洛夫语银行语音意图分类数据集
WolBanking77: Wolof Banking Speech Intent Classification Dataset
AI总结：
针对低资源语言沃洛夫语，构建银行领域意图分类数据集WolBanking77（含9791条文本和超4小时语音），实验显示基线模型表现良好，并提供F1与词错误率基准。
摘要：
意图分类模型近年来取得了显著进展。然而，以往的研究主要集中于高资源语言数据集，这导致低资源语言以及文盲率高的地区存在空白，在这些地区，语言更多是口头使用而非书面阅读或书写。例如，塞内加尔的情况就是如此，该国约90%的人口使用沃洛夫语，而全国文盲率仍高达42%。实际上，沃洛夫语在西非地区有超过1000万的使用者。为了解决这些局限性，我们引入了沃洛夫语银行语音意图分类数据集（WolBanking77），用于意图分类的学术研究。WolBanking77目前包含银行领域的9,791条文本句子和超过4小时的口语句子。本研究在多种基线上进行了实验，包括文本和语音的最先进模型。在当前数据集上，结果非常有前景。此外，本文对数据集内容进行了深入检查。我们分别报告了在WolBanking77数据集上训练的自然语言处理（NLP）和自动语音识别（ASR）模型的基线F1分数和词错误率指标，以及模型之间的比较。数据集和代码可在以下网址获取：https://github.com/abdoukarim/wolbanking77。
英文摘要：
Intent classification models have made a significant progress in recent years. However, previous studies primarily focus on high-resource language datasets, which results in a gap for low-resource languages and for regions with high rates of illiteracy, where languages are more spoken than read or written. This is the case in Senegal, for example, where Wolof is spoken by around 90\% of the population, while the national illiteracy rate remains at of 42\%. Wolof is actually spoken by more than 10 million people in West African region. To address these limitations, we introduce the Wolof Banking Speech Intent Classification Dataset (WolBanking77), for academic research in intent classification. WolBanking77 currently contains 9,791 text sentences in the banking domain and more than 4 hours of spoken sentences. Experiments on various baselines are conducted in this work, including text and voice state-of-the-art models. The results are very promising on this current dataset. In addition, this paper presents an in-depth examination of the dataset's contents. We report baseline F1-scores and word error rates metrics respectively on NLP and ASR models trained on WolBanking77 dataset and also comparisons between models. Dataset and code available at: https://github.com/abdoukarim/wolbanking77.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
