no code implementations • 31 Oct 2022 • Nyoungwoo Lee, ChaeHun Park, Ho-Jin Choi, Jaegul Choo
To overcome these limitations, this paper proposes a simple but efficient method for generating adversarial negative responses leveraging a large-scale language model.
no code implementations • 14 Sep 2022 • Bum Chul Kwon, Jungsoo Lee, Chaeyeon Chung, Nyoungwoo Lee, Ho-Jin Choi, Jaegul Choo
We call the unwanted correlations "data biases," and the visual features causing data biases "bias factors."
no code implementations • 1 Sep 2021 • Nyoungwoo Lee, ChaeHun Park, Ho-Jin Choi
In open-domain dialogues, predictive uncertainties are mainly evaluated in a domain shift setting to cope with out-of-distribution inputs.
1 code implementation • ACL 2021 • Nyoungwoo Lee, Suwon Shin, Jaegul Choo, Ho-Jin Choi, Sung-Hyun Myaeng
In multi-modal dialogue systems, it is important to allow the use of images as part of a multi-turn conversation.