Talk: Toward human-centric language generation systems

Abstract: Natural language generation (NLG) is a key component of many language technology applications such as dialogue systems, question-answering systems, automatic email replies, and story generation systems. Despite the recent advances of massive language models like GPT3, texts predicted by such systems are far from any human-like language. In fact, they most often produce either nonfactual text, incoherent text, or pragmatically inappropriate text. Also, the lack of interaction with real users makes the system less controllable and nonpractical. To address these problems, my research is focused on developing linguistically informed computational models in a wide range of generation tasks and building real-world NLG systems which can interact with humans. In this talk, I propose three steps to develop human-centric language generation systems: (i) Studying linguistic theories, (ii) Developing theory-informed models, and (iii) Building human-machine cooperative systems. My research lies at the intersection of three fields: computational linguistics as a theoretical basis, modern machine learning as a powerful technical tool, and human-computer interaction as a robust, reliable interactive testbed.

Bio: Dongyeop Kang is a postdoctoral scholar at the University of California, Berkeley. He obtained his Ph.D. in the Language Technologies Institute of the School of Computer Science at Carnegie Mellon University. His Ph.D. study has been supported by Allen Institute for AI (AI2) fellowship, CMU presidential fellowship, and ILJU graduate fellowship. During the study, he interned at Facebook AI research, AI2, and Microsoft Research.