In recent advancements in spoken question answering (QA), end-to-end models
have made significant strides. However, previous research has primarily focused
on extractive span selection. While this extractive-based approach is effective
when answers are present directly within the input, it falls short in
addressing abstractive questions, where answers are not directly extracted but
inferred from the given information. To bridge this gap, we introduce the first
end-to-end Generative Spoken Question Answering (GSQA) model that empowers the
system to engage in abstractive reasoning. The challenge in training our GSQA
model lies in the absence of a spoken abstractive QA dataset. We propose using
text models for initialization and leveraging the extractive QA dataset to
transfer knowledge from the text generative model to the spoken generative
model. Experimental results indicate that our model surpasses the previous
extractive model by 3% on extractive QA datasets. Furthermore, the GSQA model
has only been fine-tuned on the spoken extractive QA dataset. Despite not
having seen any spoken abstractive QA data, it can still closely match the
performance of the cascade model. In conclusion, our GSQA model shows the
potential to generalize to a broad spectrum of questions, thus further
expanding spoken question answering capabilities of abstractive QA. Our code is
available at
\href{https://voidful.github.io/GSQA}{this https URL}

近期关于口语问答方面的研究表明，最新的端到端模型在提问回答方面取得了显著的进展，尤其是在提取性问题的选择上。然而，先前的研究主要集中在提取性问题上，这种基于提取的方法在直接提取答案的情况下是有效的，但在涉及抽象问题且答案不可直接提取的情况下则存在不足。为了弥补这一差距，我们首次引入了第一个端到端生成式口语问答模型（GSQA），以增强系统的抽象推理能力。我们的 GSQA 模型训练的挑战在于缺乏口语抽象问答数据集。我们建议使用文本模型进行初始化，并利用基于提取的问答数据集将文本生成模型的知识转移到口语生成模型上。实验结果表明，我们的模型在基于提取的问答数据集上的性能超过了先前的提取性模型 3％。此外，GSQA 模型仅在口语提取性问答数据集上进行了微调。尽管没有见过任何口语抽象问答数据，它仍然能够与级联模型的性能高度匹配。总而言之，我们的 GSQA 模型展示了到广泛问题范围的泛化潜力，从而进一步扩展了抽象问答的口语问答能力。