no code implementations • 18 Jan 2023 • Hyungtak Choi, Hyeonmok Ko, Gurpreet Kaur, Lohith Ravuru, Kiranmayi Gandikota, Manisha Jhawar, Simma Dharani, Pranamya Patil
Our evaluations of the dialogue datasets between users that plan a schedule show that our model outperforms the baseline model.
no code implementations • COLING 2020 • Pawel Bujnowski, Kseniia Ryzhova, Hyungtak Choi, Katarzyna Witkowska, Jaroslaw Piersa, Tymoteusz Krumholc, Katarzyna Beksa
The topic of this paper is neural multi-task training for text style transfer.
no code implementations • WS 2019 • Hyungtak Choi, Lohith Ravuru, Tomasz Dryja{\'n}ski, Sunghan Rye, Dong-Hyun Lee, Hojung Lee, Inchul Hwang
This paper describes our submission to the TL;DR challenge.
no code implementations • 27 Aug 2019 • Heriberto Cuayáhuitl, Donghyeon Lee, Seonghan Ryu, Yongjin Cho, Sungja Choi, Satish Indurthi, Seunghak Yu, Hyungtak Choi, Inchul Hwang, Jihie Kim
Experimental results using chitchat data reveal that (1) near human-like dialogue policies can be induced, (2) generalisation to unseen data is a difficult problem, and (3) training an ensemble of chatbot agents is essential for improved performance over using a single agent.
no code implementations • WS 2018 • Hyungtak Choi, Siddarth K.M., Haehun Yang, Heesik Jeon, Inchul Hwang, Jihie Kim
In this paper, we propose a self-learning architecture for generating natural language templates for conversational assistants.