# ODSQA: Open-domain Spoken Question Answering Dataset

arXiv: https://arxiv.org/abs/1808.02280 · HTML: https://mlsanity.com/paper/1808.02280/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2018.

## Built on by (most cited first, 21 of 21)

- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [Pano-AVQA: Grounded Audio-Visual Question Answering on 360^circ Videos](https://mlsanity.com/paper/2110.05122/) (2021)
- [Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering](https://mlsanity.com/paper/2109.03381/) (2021)
- [SD-QA: Spoken Dialectal Question Answering for the Real World](https://mlsanity.com/paper/2109.12072/) (2021)
- [End-to-end Spoken Conversational Question Answering: Task, Dataset and Model](https://mlsanity.com/paper/2204.14272/) (2022)
- [Self-supervised Dialogue Learning for Spoken Conversational Question Answering](https://mlsanity.com/paper/2106.02182/) (2021)
- [NoiseQA: Challenge Set Evaluation for User-Centric Question Answering](https://mlsanity.com/paper/2102.08345/) (2021)
- [HeySQuAD: A Spoken Question Answering Dataset](https://mlsanity.com/paper/2304.13689/) (2023)
- [Revisiting the Boundary between ASR and NLU in the Age of Conversational Dialog Systems](https://mlsanity.com/paper/2112.05842/) (2021)
- [DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken Question Answering](https://mlsanity.com/paper/2203.04911/) (2022)
- [LibriSQA: A Novel Dataset and Framework for Spoken Question Answering with Large Language Models](https://mlsanity.com/paper/2308.10390/) (2023)
- [SpeechDPR: End-to-End Spoken Passage Retrieval for Open-Domain Spoken Question Answering](https://mlsanity.com/paper/2401.13463/) (2024)
- [Video-Guided Curriculum Learning for Spoken Video Grounding](https://mlsanity.com/paper/2209.00277/) (2022)
- [Leveraging Pretrained ASR Encoders for Effective and Efficient End-to-End Speech Intent Classification and Slot Filling](https://mlsanity.com/paper/2307.07057/) (2023)
- [Learning Action-Effect Dynamics for Hypothetical Vision-Language Reasoning Task](https://mlsanity.com/paper/2212.03866/) (2022)
- [End-to-end Contrastive Language-Speech Pretraining Model For Long-form Spoken Question Answering](https://mlsanity.com/paper/2511.09282/) (2025)
- [An Initial Investigation of Non-Native Spoken Question-Answering](https://mlsanity.com/paper/2107.04691/) (2021)
- [Spoken question answering for visual queries](https://mlsanity.com/paper/2505.23308/) (2025)
- [Rhapsody: A Dataset for Highlight Detection in Podcasts](https://mlsanity.com/paper/2505.19429/) (2025)
- [INSPIRE: A Benchmark for Instruction-Aware Speech Retrieval](https://mlsanity.com/paper/2608.16203/) (2026)
- [Better Retrieval, Worse Robustness: How Multi-hop RAG Amplifies Upstream ASR Errors](https://mlsanity.com/paper/2608.22872/) (2026)

## Abstract (arXiv)

Reading comprehension by machine has been widely studied, but machine comprehension of spoken content is still a less investigated problem. In this paper, we release Open-Domain Spoken Question Answering Dataset (ODSQA) with more than three thousand questions. To the best of our knowledge, this is the largest real SQA dataset. On this dataset, we found that ASR errors have catastrophic impact on SQA. To mitigate the effect of ASR errors, subword units are involved, which brings consistent improvements over all the models. We further found that data augmentation on text-based QA training examples can improve SQA.