24 citations · 51 across the 16 of their papers we have counts for
15 papers · 1 filter
FreeTalky: Don't Be Afraid! Conversations Made Easier by a Humanoid Robot using Persona-based Dialogue
Chanjun Park, Yoonna Jang, Seolhwa Lee +2
We propose a deep learning-based foreign language learning platform, named FreeTalky, for people who experience anxiety dealing with foreign languages, by employing a humanoid robo…
Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge
Yoonna Jang, Jungwoo Lim, Yuna Hur +6
Humans usually have conversations by making use of prior knowledge about a topic and background information of the people whom they are talking to. However, existing conversational…
A Self-Supervised Automatic Post-Editing Data Generation Tool
Hyeonseok Moon, Chanjun Park, Sugyeong Eo +3
Data building for automatic post-editing (APE) requires extensive and expert-level human effort, as it contains an elaborate process that involves identifying errors in sentences a…
A New Tool for Efficiently Generating Quality Estimation Datasets
Sugyeong Eo, Chanjun Park, Jaehyung Seo +2
Building of data for quality estimation (QE) training is expensive and requires significant human labor. In this study, we focus on a data-centric approach while performing QE, and…
Automatic Knowledge Augmentation for Generative Commonsense Reasoning
Jaehyung Seo, Chanjun Park, Sugyeong Eo +2
Generative commonsense reasoning is the capability of a language model to generate a sentence with a given concept-set that is based on commonsense knowledge. However, generative l…
How should human translation coexist with NMT? Efficient tool for building high quality parallel corpus
Chanjun Park, Seolhwa Lee, Hyeonseok Moon +3
This paper proposes a tool for efficiently constructing high-quality parallel corpora with minimizing human labor and making this tool publicly available. Our proposed construction…
Empirical Analysis of Korean Public AI Hub Parallel Corpora and in-depth Analysis using LIWC
Chanjun Park, Midan Shim, Sugyeong Eo +4
Machine translation (MT) system aims to translate source language into target language. Recent studies on MT systems mainly focus on neural machine translation (NMT). One factor th…
Who speaks like a style of Vitamin: Towards Syntax-Aware DialogueSummarization using Multi-task Learning
Seolhwa Lee, Kisu Yang, Chanjun Park +2
Abstractive dialogue summarization is a challenging task for several reasons. First, most of the important pieces of information in a conversation are scattered across utterances t…
PicTalky: Augmentative and Alternative Communication Software for Language Developmental Disabilities
Chanjun Park, Yoonna Jang, Seolhwa Lee +3
Augmentative and alternative communication (AAC) is a practical means of communication for people with language disabilities. In this study, we propose PicTalky, which is an AI-bas…
I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning
Jungwoo Lim, Dongsuk Oh, Yoonna Jang +2
CommonsenseQA is a task in which a correct answer is predicted through commonsense reasoning with pre-defined knowledge. Most previous works have aimed to improve the performance w…
An Evaluation Protocol for Generative Conversational Systems
Seolhwa Lee, Heuiseok Lim, João Sedoc
There is a multitude of novel generative models for open-domain conversational systems; however, there is no systematic evaluation of different systems. Systematic comparisons requ…
An Effective Domain Adaptive Post-Training Method for BERT in Response Selection
Taesun Whang, Dongyub Lee, Chanhee Lee +3
We focus on multi-turn response selection in a retrieval-based dialog system. In this paper, we utilize the powerful pre-trained language model Bi-directional Encoder Representatio…
EmotionX-KU: BERT-Max based Contextual Emotion Classifier
Kisu Yang, Dongyub Lee, Taesun Whang +2
We propose a contextual emotion classifier based on a transferable language model and dynamic max pooling, which predicts the emotion of each utterance in a dialogue. A representat…
Character-Level Feature Extraction with Densely Connected Networks
Chanhee Lee, Young-Bum Kim, Dongyub Lee +1
Generating character-level features is an important step for achieving good results in various natural language processing tasks. To alleviate the need for human labor in generatin…
Rich Character-Level Information for Korean Morphological Analysis and Part-of-Speech Tagging
Andrew Matteson, Chanhee Lee, Young-Bum Kim +1
Due to the fact that Korean is a highly agglutinative, character-rich language, previous work on Korean morphological analysis typically employs the use of sub-character features k…