{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:35:23Z","timestamp":1777696523050,"version":"3.51.4"},"reference-count":10,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2023,11,20]]},"abstract":"<jats:p>Question Answering based on Tabular and Textual data is a novel task proposed in recent years in the field of QA. At present, most QA systems return answers from a single data form, such as knowledge graphs, tables, texts. However, hybrid data including structured and unstructured data is quite pervasive in real life instead of a single form. Recent research on TAT-QA mainly suffers from the higher error of extracting supporting evidences from both tabular and textual content. This paper aimed to address the problem of failure evidence extraction from more complex and realistic hybrid data. We first proposed two types of metrics to evaluate the performance of evidence extraction on hybrid data, i.e. wrong evidence ratio (WER) and missing evidence ratio (MER). Then we utilize a candidate extractor to obtain supporting evidence related to the question. Third, an origin selector is designed to determine from where the question\u2019s answer comes. Finally, the loss of origin selector is fused to the final loss function, which can improve the evidence extraction performance. Experimental results on the TAT-QA dataset showed that our proposed model outperforms the best baseline in terms of F1, WER and MER, which proves the effectiveness of our model.<\/jats:p>","DOI":"10.3233\/ida-227032","type":"journal-article","created":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T11:40:24Z","timestamp":1701171624000},"page":"1839-1852","source":"Crossref","is-referenced-by-count":1,"title":["Multi-head attention based candidate segment selection in QA over hybrid data"],"prefix":"10.1177","volume":"27","author":[{"given":"Qian","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi, China"},{"name":"Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, Shanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoying","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tongji University, Jiading, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suge","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi, China"},{"name":"Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, Shanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-227032_ref1","doi-asserted-by":"crossref","unstructured":"P. Wu, X. Zhang and Z. Feng, A Survey of Question Answering over Knowledge Base, in: China Conference on Knowledge Graph and Semantic Computing, 2019.","DOI":"10.1007\/978-981-15-1956-7_8"},{"key":"10.3233\/IDA-227032_ref2","doi-asserted-by":"crossref","first-page":"94341","DOI":"10.1109\/ACCESS.2020.2988903","article-title":"Recent trends in deep learning based open-domain textual question answering systems","volume":"8","author":"Huang","year":"2020","journal-title":"IEEE Access"},{"key":"10.3233\/IDA-227032_ref8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3380954","article-title":"Large-scale question tagging via joint question-topic embedding learning","volume":"38","author":"Nie","year":"2020","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"key":"10.3233\/IDA-227032_ref11","doi-asserted-by":"crossref","unstructured":"S. Zhang and K. Balog, Auto-completion for Data Cells in Relational Tables, in: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019.","DOI":"10.1145\/3357384.3357932"},{"key":"10.3233\/IDA-227032_ref12","doi-asserted-by":"crossref","unstructured":"S. Zhang, Z. Dai, K. Balog and J. Callan, Summarizing and Exploring Tabular Data in Conversational Search, in: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020.","DOI":"10.1145\/3397271.3401205"},{"key":"10.3233\/IDA-227032_ref15","doi-asserted-by":"crossref","unstructured":"B. Grau and A. Ligozat, A Corpus for Hybrid Question Answering Systems, in: Companion Proceedings of the The Web Conference 2018, 2018.","DOI":"10.1145\/3184558.3191540"},{"key":"10.3233\/IDA-227032_ref22","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1162\/tacl_a_00097","article-title":"ABCNN: Attention-based convolutional neural network for modeling sentence pairs","volume":"4","author":"Yin","year":"2016","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"10.3233\/IDA-227032_ref23","doi-asserted-by":"crossref","unstructured":"N.K. Tran and C. Nieder\u00e9e, Multihop Attention Networks for Question Answer Matching, in: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 2018.","DOI":"10.1145\/3209978.3210009"},{"key":"10.3233\/IDA-227032_ref24","first-page":"109","article-title":"Evidence sentence extraction for reading comprehension based on multi-module","volume":"6","author":"Ji","year":"2022","journal-title":"Journal of Chinese Information Processing"},{"key":"10.3233\/IDA-227032_ref29","doi-asserted-by":"crossref","unstructured":"X. Jin, W. Lei, Z. Ren, H. Chen, S. Liang, Y.E. Zhao and D. Yin, Explicit State Tracking with Semi-Supervisionfor Neural Dialogue Generation, in: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018.","DOI":"10.1145\/3269206.3271683"}],"container-title":["Intelligent Data Analysis"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDA-227032","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:20:16Z","timestamp":1777454416000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDA-227032"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,20]]},"references-count":10,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/ida-227032","relation":{},"ISSN":["1088-467X","1571-4128"],"issn-type":[{"value":"1088-467X","type":"print"},{"value":"1571-4128","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,20]]}}}