no code implementations • 3 Apr 2024 • Yejin Jeon, Yunsu Kim, Gary Geunbae Lee
Contemporary neural speech synthesis models have indeed demonstrated remarkable proficiency in synthetic speech generation as they have attained a level of quality comparable to that of human-produced speech.
1 code implementation • 31 Mar 2024 • Seonjeong Hwang, Yunsu Kim, Gary Geunbae Lee
We also prove that our model logically and incrementally increases the complexity of questions, and the generated multi-hop questions are also beneficial for training question answering models.
1 code implementation • 26 Mar 2024 • Deokhyung Kang, Baikjin Jung, Yunsu Kim, Gary Geunbae Lee
Previous studies in table-text open-domain question answering have two common challenges: firstly, their retrievers can be affected by false-positive labels in training datasets; secondly, they may struggle to provide appropriate evidence for questions that require reasoning across the table.
1 code implementation • 13 Mar 2024 • Heejin Do, Yunsu Kim, Gary Geunbae Lee
Recently, encoder-only pre-trained models such as BERT have been successfully applied in automated essay scoring (AES) to predict a single overall score.
no code implementations • 6 Dec 2023 • Wonjun Lee, Gary Geunbae Lee, Yunsu Kim
This research contributes to the advancements of two-pass ASR systems in low-resource languages, offering the potential for improved cross-lingual transfer learning.
1 code implementation • 4 Dec 2023 • Jihyun Lee, Yejin Jeon, Wonjun Lee, Yunsu Kim, Gary Geunbae Lee
We address this by investigating synthetic audio data for audio-based DST.
1 code implementation • 26 May 2023 • Heejin Do, Yunsu Kim, Gary Geunbae Lee
With rapid technological growth, automatic pronunciation assessment has transitioned toward systems that evaluate pronunciation in various aspects, such as fluency and stress.
1 code implementation • 26 May 2023 • Heejin Do, Yunsu Kim, Gary Geunbae Lee
Thus, predicting various trait scores of unseen-prompt essays (called cross-prompt essay trait scoring) is a remaining challenge of AES.
no code implementations • 17 Mar 2023 • Jihyun Lee, Seungyeon Seo, Yunsu Kim, Gary Geunbae Lee
We present our work on Track 2 in the Dialog System Technology Challenges 11 (DSTC11).
no code implementations • 17 Nov 2022 • Jihyun Lee, Chaebin Lee, Yunsu Kim, Gary Geunbae Lee
In dialogue state tracking (DST), labeling the dataset involves considerable human labor.
1 code implementation • 15 Nov 2022 • Heejin Do, Yunsu Kim, Gary Geunbae Lee
In this paper, we propose a Hierarchical Pronunciation Assessment with Multi-aspect Attention (HiPAMA) model, which hierarchically represents the granularity levels to directly capture their linguistic structures and introduces multi-aspect attention that reflects associations across aspects at the same level to create more connotative representations.
no code implementations • 24 Oct 2022 • Seonjeong Hwang, Yunsu Kim, Gary Geunbae Lee
Conversational question answering (CQA) facilitates an incremental and interactive understanding of a given context, but building a CQA system is difficult for many domains due to the problem of data scarcity.
no code implementations • COLING 2022 • Hyunmin Jeon, Gary Geunbae Lee
In this paper, we propose Schema Encoding for Transferable Dialogue State Tracking (SETDST), which is a neural DST method for effective transfer to new domains.
no code implementations • COLING 2022 • Seonjeong Hwang, Gary Geunbae Lee
Conversational question--answer generation is a task that automatically generates a large-scale conversational question answering dataset based on input passages.
no code implementations • 16 Sep 2022 • Jihyun Lee, Gary Geunbae Lee
Few-shot dialogue state tracking (DST) model tracks user requests in dialogue with reliable accuracy even with a small amount of data.
no code implementations • 9 Aug 2022 • Choonghan Kim, Gary Geunbae Lee
We observed that when the same target sentence was repeated twice, Transformer (T5) based model generates an output made up of asymmetric sentences from structured inputs.
1 code implementation • 7 Jul 2021 • Hyunmin Jeon, Gary Geunbae Lee
In this paper, we propose a multi-domain task-oriented dialogue system, called Dialogue System with Optimizing a Recurrent Action Policy using Efficient Context (DORA), that uses SL, with subsequently applied RL to optimize dialogue systems using a recurrent dialogue policy.
no code implementations • 11 Mar 2021 • Hyunmin Jeon, Gary Geunbae Lee
In this paper, we present DoTS (Domain State Tracking for a Simplified Dialogue System), a task-oriented dialogue system that uses a simplified input context instead of the entire dialogue history.
no code implementations • EMNLP 2018 • Seonghan Ryu, Sangjun Koo, Hwanjo Yu, Gary Geunbae Lee
The main goal of this paper is to develop out-of-domain (OOD) detection for dialog systems.
Generative Adversarial Network Out of Distribution (OOD) Detection +1
2 code implementations • 27 Jul 2018 • Seonghan Ryu, Seokhwan Kim, Junhwi Choi, Hwanjo Yu, Gary Geunbae Lee
Then we used domain-category analysis as an auxiliary task to train neural sentence embedding for OOD sentence detection.
no code implementations • 25 May 2016 • Byung-soo Kim, Hwanjo Yu, Gary Geunbae Lee
To the best of our knowledge, this is the first work to apply deep learning to Open IE.
no code implementations • LREC 2012 • Hongsuck Seo, Kyusong Lee, Gary Geunbae Lee, Soo-Ok Kweon, Hae-Ri Kim
The goal of our research is to build a grammatical error-tagged corpus for Korean learners of Spoken English dubbed Postech Learner Corpus.