GenNI: Human-AI Collaboration for Data-Backed Text Generation

被引:12
作者
Strobelt, Hendrik [1 ,2 ]
Kinley, Jambay [3 ]
Krueger, Robert [4 ]
Beyer, Johanna [4 ]
Pfister, Hanspeter [4 ]
Rush, Alexander M. [3 ]
机构
[1] IBM Res, Cambridge, MA 02142 USA
[2] MIT IBM Watson AI Lab, Cambridge, MA 02142 USA
[3] Cornell Univ, New York, NY 10021 USA
[4] Harvard Univ, Cambridge, MA 02138 USA
关键词
Computational modeling; Visualization; Tools; Data models; Collaboration; Task analysis; Deep learning; Tabular Data; Text; Document Data; Machine Learning; Statistics; Modelling; Simulation Applications;
D O I
10.1109/TVCG.2021.3114845
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Table2Text systems generate textual output based on structured data utilizing machine learning. These systems are essential for fluent natural language interfaces in tools such as virtual assistants; however, left to generate freely these ML systems often produce misleading or unexpected outputs. GenNI (Generation Negotiation Interface) is an interactive visual system for high-level human-AI collaboration in producing descriptive text. The tool utilizes a deep learning model designed with explicit control states. These controls allow users to globally constrain model generations, without sacrificing the representation power of the deep learning models. The visual interface makes it possible for users to interact with AI systems following a Refine-Forecast paradigm to ensure that the generation system acts in a manner human users find suitable. We report multiple use cases on two experiments that improve over uncontrolled generation approaches, while at the same time providing fine-grained control. A demo and source code are available at https://genni.vizhub.ai.
引用
收藏
页码:1106 / 1116
页数:11
相关论文
共 63 条
  • [1] [Anonymous], 2017, P 2017 C EMPIRICAL M
  • [2] Belz A, 2007, HLTNAACL
  • [3] Bouayad-Agha N., 2011, ENLG
  • [4] Busemann S, 1998, FLEXIBLE SHALLOW APP
  • [5] C. D. C. Ltd, TABNINE COD FAST AI
  • [6] Cahill L., 2000, SEARCH REFERENCE ARC
  • [7] RNNbow: Visualizing Learning Via Backpropagation Gradients in RNNs
    Cashman, Dylan
    Patterson, Genevieve
    Mosca, Abigail
    Watts, Nathan
    Robinson, Shannon
    Chang, Remco
    [J]. IEEE COMPUTER GRAPHICS AND APPLICATIONS, 2018, 38 (06) : 39 - 50
  • [8] Chan A. T. S, 2020, P INT C LEARN REPR A
  • [9] Chen MD, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P5972
  • [10] Colin E., 2018, EMNLP, DOI [10.18653/v1/D18-1113, DOI 10.18653/V1/D18-1113]