23 citations · 25 across the 6 of their papers we have counts for
6 papers
Measuring and Improving Semantic Diversity of Dialogue Generation
Seungju Han, Beomsu Kim, Buru Chang
Response diversity has become an important criterion for evaluating the quality of open-domain dialogue generation models. However, current evaluation metrics for response diversit…
Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional Characters with only a Few Utterances
Seungju Han, Beomsu Kim, Jin Yong Yoo +4
In this paper, we consider mimicking fictional characters as a promising direction for building engaging conversation models. To this end, we present a new practical task where onl…
Distilling the Knowledge of Large-scale Generative Models into Retrieval Models for Efficient Open-domain Conversation
Beomsu Kim, Seokjun Seo, Seungju Han +2
Despite the remarkable performance of large-scale generative models in open-domain conversation, they are known to be less practical for building real-time conversation systems due…
Efficient Click-Through Rate Prediction for Developing Countries via Tabular Learning
Joonyoung Yi, Buru Chang
Despite the rapid growth of online advertisement in developing countries, existing highly over-parameterized Click-Through Rate (CTR) prediction models are difficult to be deployed…
"Killing Me" Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanism
Buru Chang, Inggeol Lee, Hyunjae Kim +1
Several machine learning-based spoiler detection models have been proposed recently to protect users from spoilers on review websites. Although dependency relations between context…
Disentangling Label Distribution for Long-tailed Visual Recognition
Youngkyu Hong, Seungju Han, Kwanghee Choi +3
The current evaluation protocol of long-tailed visual recognition trains the classification model on the long-tailed source label distribution and evaluates its performance on the…