{"title":"Intentable:基于意图的图表标题的混合主动系统","authors":"Ji-Won Choi, Jaemin Jo","doi":"10.1109/VIS54862.2022.00017","DOIUrl":null,"url":null,"abstract":"We present Intentable, a mixed-initiative caption authoring system that allows the author to steer an automatic caption generation pro-cess to reflect their intent, e.g., the finding that the author gained from visualization and thus wants to write a caption for. We first derive a grammar for specifying the intent, i.e., a caption recipe, and build a neural network that generates caption sentences given a recipe. Our quantitative evaluation revealed that our intent-based generation system not only allows the author to engage in the generation process but also produces more fluent captions than the previous end-to-end approaches without user intervention. Finally, we demonstrate the versatility of our system, such as context adaptation, unit conversion, and sentence reordering.","PeriodicalId":190244,"journal":{"name":"2022 IEEE Visualization and Visual Analytics (VIS)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Intentable: A Mixed-Initiative System for Intent-Based Chart Captioning\",\"authors\":\"Ji-Won Choi, Jaemin Jo\",\"doi\":\"10.1109/VIS54862.2022.00017\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present Intentable, a mixed-initiative caption authoring system that allows the author to steer an automatic caption generation pro-cess to reflect their intent, e.g., the finding that the author gained from visualization and thus wants to write a caption for. We first derive a grammar for specifying the intent, i.e., a caption recipe, and build a neural network that generates caption sentences given a recipe. Our quantitative evaluation revealed that our intent-based generation system not only allows the author to engage in the generation process but also produces more fluent captions than the previous end-to-end approaches without user intervention. Finally, we demonstrate the versatility of our system, such as context adaptation, unit conversion, and sentence reordering.\",\"PeriodicalId\":190244,\"journal\":{\"name\":\"2022 IEEE Visualization and Visual Analytics (VIS)\",\"volume\":\"114 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE Visualization and Visual Analytics (VIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/VIS54862.2022.00017\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Visualization and Visual Analytics (VIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VIS54862.2022.00017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Intentable: A Mixed-Initiative System for Intent-Based Chart Captioning
We present Intentable, a mixed-initiative caption authoring system that allows the author to steer an automatic caption generation pro-cess to reflect their intent, e.g., the finding that the author gained from visualization and thus wants to write a caption for. We first derive a grammar for specifying the intent, i.e., a caption recipe, and build a neural network that generates caption sentences given a recipe. Our quantitative evaluation revealed that our intent-based generation system not only allows the author to engage in the generation process but also produces more fluent captions than the previous end-to-end approaches without user intervention. Finally, we demonstrate the versatility of our system, such as context adaptation, unit conversion, and sentence reordering.