122 citations · 201 across the 6 of their papers we have counts for
6 papers
Don't Contrast the Impossible: Region-Constrained Batching for Contrastive User Modeling on a Local Community Platform
Seungho Han, Byeongchang Kim, Jin Yu
Contrastive learning is widely used for user modeling in large-scale recommender systems, where standard in-batch negatives implicitly assume universal exposure that any user can b…
Model-based Offline Reinforcement Learning with Count-based Conservatism
Byeongchan Kim, Min-hwan Oh
In this paper, we propose a model-based offline reinforcement learning method that integrates count-based conservatism, named . Our method utilizes the count e…
Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness
Hyunwoo Kim, Byeongchang Kim, Gunhee Kim
We explore the task of improving persona consistency of dialogue agents. Recent models tackling consistency often train with additional Natural Language Inference (NLI) labels or a…
Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue
Byeongchang Kim, Jaewoo Ahn, Gunhee Kim
Knowledge-grounded dialogue is a task of generating an informative response based on both discourse context and external knowledge. As we focus on better modeling the knowledge sel…
Abstractive Summarization of Reddit Posts with Multi-level Memory Networks
Byeongchang Kim, Hyunwoo Kim, Gunhee Kim
We address the problem of abstractive summarization in two directions: proposing a novel dataset and a new model. First, we collect Reddit TIFU dataset, consisting of 120K posts fr…
Attend to You: Personalized Image Captioning with Context Sequence Memory Networks
Cesc Chunseong Park, Byeongchang Kim, Gunhee Kim
We address personalization issues of image captioning, which have not been discussed yet in previous research. For a query image, we aim to generate a descriptive sentence, account…