# Self-supervised Dialogue Learning for Spoken Conversational Question Answering

Source: [Self-supervised Dialogue Learning for Spoken Conversational Question Answering](<https://www.sota2.com/research/paper/68fa6a381e209131cea26b49>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:19:37.191Z

Authors: Nuo Chen, Chenyu You, Yuexian Zou

Published: 2021-06-04T00:09:38.000Z

## Abstract

In spoken conversational question answering (SCQA), the answer to the corresponding question is generated by retrieving and then analyzing a fixed spoken document, including multi-part conversations. Most SCQA systems have considered only retrieving information from ordered utterances. However, the sequential order of dialogue is important to build a robust spoken conversational question answering system, and the changes of utterances order may severely result in low-quality and incoherent corpora. To this end, we introduce a self-supervised learning approach, including incoherence discrimination, insertion detection, and question prediction, to explicitly capture the coreference resolution and dialogue coherence among spoken documents. Specifically, we design a joint learning framework where the auxiliary self-supervised tasks can enable the pre-trained SCQA systems towards more coherent and meaningful spoken dialogue learning. We also utilize the proposed self-supervised learning tasks to capture intra-sentence coherence. Experimental results demonstrate that our proposed method provides more coherent, meaningful, and appropriate responses, yielding superior performance gains compared to the original pre-trained language models. Our method achieves state-of-the-art results on the Spoken-CoQA dataset.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2106.02182>)

[PDF](<https://arxiv.org/pdf/2106.02182.pdf>)