{"title":"Automated performance assessment in interactive QA","authors":"J. Chai, Tyler Baldwin, Chen Zhang","doi":"10.1145/1148170.1148290","DOIUrl":null,"url":null,"abstract":"In interactive question answering (QA), users and systems take turns to ask questions and provide answers. In such an interactive setting, user questions largely depend on the answers provided by the system. One question is whether user follow-up questions can provide feedback for the system to automatically assess its performance (e.g., assess whether a correct answer is delivered). This self-awareness can make QA systems more intelligent for information seeking, for example, by adapting better strategies to cope with problematic situations. Therefore, this paper describes our initial investigation in addressing this problem. Our results indicate that interaction context can provide useful cues for automated performance assessment in interactive QA.","PeriodicalId":433366,"journal":{"name":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1148170.1148290","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In interactive question answering (QA), users and systems take turns to ask questions and provide answers. In such an interactive setting, user questions largely depend on the answers provided by the system. One question is whether user follow-up questions can provide feedback for the system to automatically assess its performance (e.g., assess whether a correct answer is delivered). This self-awareness can make QA systems more intelligent for information seeking, for example, by adapting better strategies to cope with problematic situations. Therefore, this paper describes our initial investigation in addressing this problem. Our results indicate that interaction context can provide useful cues for automated performance assessment in interactive QA.