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CtrlGen: Controllable Generative Modeling in Language and Vision
Steven Y. Feng · Dor Arad Hudson · Tatsunori Hashimoto · DONGYEOP Kang · Varun Prashant Gangal · Anusha Balakrishnan · Joel Tetreault

Mon Dec 13 08:00 AM -- 12:00 AM (PST) @
Event URL: https://ctrlgenworkshop.github.io/ »

Over the past few years, there has been an increased interest in the areas of language and image generation within the community. As generated texts by models like GPT-3 start to sound more fluid and natural, and generated images and videos by GAN models appear more realistic, researchers began focusing on qualitative properties of the generated content such as the ability to control its style and structure, or incorporate information from external sources into the output. Such aims are extremely important to make language and image generation useful for human-machine interaction and other real-world applications including machine co-creativity, entertainment, reducing biases or toxicity, and improving conversational agents and personal assistants.

Achieving these ambitious but important goals introduces challenges not only from NLP and Vision perspectives, but also ones that pertain to Machine Learning as a whole, which has witnessed a growing body of research in relevant domains such as interpretability, disentanglement, robustness, and representation learning. We believe that progress towards the realization of human-like language and image generation may benefit greatly from insights and progress in these and other ML areas.

In this workshop, we propose to bring together researchers from the NLP, Vision, and ML communities to discuss the current challenges and explore potential directions for controllable generation and improve its quality, correctness, and diversity. As excitement about language and image generation has significantly increased recently thanks to the advent and improvement of language models, Transformers, and GANs, we feel this is the opportune time to hold a new workshop about this subject. We hope CtrlGen will foster discussion and interaction across communities, and sprout fruitful cross-domain relations that open the door for enhanced controllability in language and image generation.

Author Information

Steven Y. Feng (Carnegie Mellon University)
Dor Arad Hudson (Stanford University)
Tatsunori Hashimoto (Stanford)
DONGYEOP Kang (UC Berkeley)
Varun Prashant Gangal (Carnegie Mellon University)
Anusha Balakrishnan (Microsoft)
Joel Tetreault (Dataminr)

Joel Tetreault is Director of Research at Dataminr, a company that provides updates on breaking events across the world in near real-time. His background is in AI, specifically Natural Language Processing and Machine Learning, and using techniques from those fields to solve real-world problems such as automatic essay scoring, grammatical error correction, hate speech detection, ranking user comments and dialogue systems, among others. Prior to joining Dataminr, he led research groups at Grammarly, Nuance and Educational Testing Service, and was a Senior Research Scientist at Yahoo Labs. Joel recently finished a six-year stint as NAACL Treasurer and was a long-time organizer of the Building Educational Application workshop series. He is a program chair for ACL 2020. He received his PhD in Computer Science from the University of Rochester and his Bachelor's degree from Harvard University.

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