In spoken question answering, the systems are designed to answer questions
from contiguous text spans within the related speech transcripts. However, the
most natural way that human seek or test their knowledge is via human
conversations. Therefore, we propose a new Spoken Conversational Question
Answering task (SCQA), aiming at enabling the systems to model complex dialogue
flows given the speech documents. In this task, our main objective is to build
the system to deal with conversational questions based on the audio recordings,
and to explore the plausibility of providing more cues from different
modalities with systems in information gathering. To this end, instead of
directly adopting automatically generated speech transcripts with highly noisy
data, we propose a novel unified data distillation approach, DDNet, which
effectively ingests cross-modal information to achieve fine-grained
representations of the speech and language modalities. Moreover, we propose a
simple and novel mechanism, termed Dual Attention, by encouraging better
alignments between audio and text to ease the process of knowledge transfer. To
evaluate the capacity of SCQA systems in a dialogue-style interaction, we
assemble a Spoken Conversational Question Answering (Spoken-CoQA) dataset with
more than 40k question-answer pairs from 4k conversations. The performance of
the existing state-of-the-art methods significantly degrade on our dataset,
hence demonstrating the necessity of cross-modal information integration. Our
experimental results demonstrate that our proposed method achieves superior
performance in spoken conversational question answering tasks.

提出了一种新的口语会话问答任务（SCQA），旨在使系统能够模拟复杂的对话流程，以便处理音频记录中的对话式问题，并探索从不同模态提供更多线索，通过 DDNet 方法有效地摄取跨模态信息以实现语音和语言模态的细粒度表示，并通过双向关注机制鼓励更好的音频和文本之间的对齐，以简化知识转移过程。通过 Spoken-CoQA 数据集，证明了跨模态信息融合的必要性，同时证明了提出的方法在口语会话问答任务中具有卓越的性能表现。