1 code implementation • NAACL 2022 • Seungju Han, Beomsu Kim, Jin Yong Yoo, Seokjun Seo, SangBum Kim, Enkhbayar Erdenee, Buru Chang
To better reflect the style of the character, PDP builds the prompts in the form of dialog that includes the character's utterances as dialog history.
1 code implementation • NLP4ConvAI (ACL) 2022 • Seungju Han, Beomsu Kim, Seokjun Seo, Enkhbayar Erdenee, Buru Chang
Extensive experiments demonstrate that our proposed training method alleviates the drawbacks of the existing exemplar-based generative models and significantly improves the performance in terms of appropriateness and informativeness.
1 code implementation • Findings (EMNLP) 2021 • Beomsu Kim, Seokjun Seo, Seungju Han, Enkhbayar Erdenee, Buru Chang
G2R consists of two distinct techniques of distillation: the data-level G2R augments the dialogue dataset with additional responses generated by the large-scale generative model, and the model-level G2R transfers the response quality score assessed by the generative model to the score of the retrieval model by the knowledge distillation loss.
no code implementations • 30 Aug 2016 • Byungjae Lee, Enkhbayar Erdenee, Songguo Jin, Phill Kyu Rhee
The ensemble of convolutional neural network (CNN) based object detector and Lucas-Kanede Tracker (KLT) based motion detector is employed to compute the likelihoods of foreground regions as the detection responses of different object classes.
Ranked #17 on Multiple Object Tracking on KITTI Tracking test