77 citations · 96 across the 6 of their papers we have counts for
7 papers
From What to Respond to When to Respond: Timely Response Generation for Open-domain Dialogue Agents
Seongbo Jang, Minjin Jeon, Jaehoon Lee +3
While research on dialogue response generation has primarily focused on generating coherent responses conditioning on textual context, the critical question of when to respond grou…
On the Effectiveness of Integration Methods for Multimodal Dialogue Response Retrieval
Seongbo Jang, Seonghyeon Lee, Dongha Lee +1
Multimodal chatbots have become one of the major topics for dialogue systems in both research community and industry. Recently, researchers have shed light on the multimodality of…
Exploring Language Model's Code Generation Ability with Auxiliary Functions
Seonghyeon Lee, Sanghwan Jang, Seongbo Jang +2
Auxiliary function is a helpful component to improve language model's code generation ability. However, a systematic exploration of how they affect has yet to be done. In this work…
KoDialogBench: Evaluating Conversational Understanding of Language Models with Korean Dialogue Benchmark
Seongbo Jang, Seonghyeon Lee, Hwanjo Yu
As language models are often deployed as chatbot assistants, it becomes a virtue for models to engage in conversations in a user's first language. While these models are trained on…
Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence Learning
Seonghyeon Lee, Dongha Lee, Seongbo Jang +1
Recently, finetuning a pretrained language model to capture the similarity between sentence embeddings has shown the state-of-the-art performance on the semantic textual similarity…
KLUE: Korean Language Understanding Evaluation
Sungjoon Park, Jihyung Moon, Sungdong Kim +28
We introduce Korean Language Understanding Evaluation (KLUE) benchmark. KLUE is a collection of 8 Korean natural language understanding (NLU) tasks, including Topic Classification,…
An Empirical Study of Tokenization Strategies for Various Korean NLP Tasks
Kyubyong Park, Joohong Lee, Seongbo Jang +1
Typically, tokenization is the very first step in most text processing works. As a token serves as an atomic unit that embeds the contextual information of text, how to define a to…