End-to-end Spoken Conversational Question Answering: Task, Dataset and Model

Chenyu You, Nuo Chen, Fenglin Liu, Shen Ge, Xian Wu, Yuexian Zou


Abstract
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 dialogues flow 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. We first show that the performance of the existing state-of-the-art methods significantly degrade on our dataset, hence demonstrating the necessity of incorporating cross-modal information to achieve good performance gains. Our experimental results demonstrate that our proposed method achieves superior performance in spoken conversational question answering. Codes and datasets will be made publicly available.
Anthology ID:
2022.findings-naacl.91
Volume:
Findings of the Association for Computational Linguistics: NAACL 2022
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1219–1232
Language:
URL:
https://aclanthology.org/2022.findings-naacl.91
DOI:
10.18653/v1/2022.findings-naacl.91
Bibkey:
Cite (ACL):
Chenyu You, Nuo Chen, Fenglin Liu, Shen Ge, Xian Wu, and Yuexian Zou. 2022. End-to-end Spoken Conversational Question Answering: Task, Dataset and Model. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1219–1232, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
End-to-end Spoken Conversational Question Answering: Task, Dataset and Model (You et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-naacl.91.pdf
Video:
 https://aclanthology.org/2022.findings-naacl.91.mp4
Data
CoQASpoken-SQuAD