# ODSQA: Open-domain Spoken Question Answering Dataset

Source: [ODSQA: Open-domain Spoken Question Answering Dataset](<https://www.sota2.com/research/paper/68fa64a41e209131cea05ca2>)

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

Data updated: 2025-10-28T13:56:36.458Z

Authors: Chia-Hsuan Lee, Shang-Ming Wang, Huan-Cheng Chang, Hung-Yi Lee

Published: 2018-08-07T09:47:00.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1808.02280>)

[PDF](<https://arxiv.org/pdf/1808.02280.pdf>)