Xingyao Wang


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POTATO: The Portable Text Annotation Tool
Jiaxin Pei | Aparna Ananthasubramaniam | Xingyao Wang | Naitian Zhou | Apostolos Dedeloudis | Jackson Sargent | David Jurgens
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present POTATO, the Portable text annotation tool, a free, fully open-sourced annotation system that 1) supports labeling many types of text and multimodal data; 2) offers easy-to-configure features to maximize the productivity of both deployers and annotators (convenient templates for common ML/NLP tasks, active learning, keypress shortcuts, keyword highlights, tooltips); and 3) supports a high degree of customization (editable UI, inserting pre-screening questions, attention and qualification tests). Experiments over two annotation tasks suggest that POTATO improves labeling speed through its specially-designed productivity features, especially for long documents and complex tasks. POTATO is available at and will continue to be updated.


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An animated picture says at least a thousand words: Selecting Gif-based Replies in Multimodal Dialog
Xingyao Wang | David Jurgens
Findings of the Association for Computational Linguistics: EMNLP 2021

Online conversations include more than just text. Increasingly, image-based responses such as memes and animated gifs serve as culturally recognized and often humorous responses in conversation. However, while NLP has broadened to multimodal models, conversational dialog systems have largely focused only on generating text replies. Here, we introduce a new dataset of 1.56M text-gif conversation turns and introduce a new multimodal conversational model Pepe the King Prawn for selecting gif-based replies. We demonstrate that our model produces relevant and high-quality gif responses and, in a large randomized control trial of multiple models replying to real users, we show that our model replies with gifs that are significantly better received by the community.