{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/odsqa-open-domain-spoken-question-answering","title":"ODSQA: Open-domain Spoken Question Answering Dataset","arxiv_id":"1808.02280","date":"2018-08-07","proceeding":null,"authors":["Chia-Hsuan Lee","Shang-Ming Wang","Huan-Cheng Chang","Hung-Yi Lee"],"abstract":"Reading comprehension by machine has been widely studied, but machine\ncomprehension of spoken content is still a less investigated problem. In this\npaper, we release Open-Domain Spoken Question Answering Dataset (ODSQA) with\nmore than three thousand questions. To the best of our knowledge, this is the\nlargest real SQA dataset. On this dataset, we found that ASR errors have\ncatastrophic impact on SQA. To mitigate the effect of ASR errors, subword units\nare involved, which brings consistent improvements over all the models. We\nfurther found that data augmentation on text-based QA training examples can\nimprove SQA.","url_abs":"http://arxiv.org/abs/1808.02280v1","url_pdf":"http://arxiv.org/pdf/1808.02280v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"odsqa-open-domain-spoken-question-answering","repo_url":"https://github.com/chiahsuan156/ODSQA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"}],"methods":[],"datasets_introduced":[{"slug":"odsqa","name":"ODSQA","full_name":"Open-Domain Spoken Question Answering"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.02280","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}