Papers by Jin Yea Jang
Automatic Gloss-level Data Augmentation for Sign Language Translation (2022.lrec-1)
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| Challenge: | Existing methods for enhancing sign language text data are insufficient . fewer studies have been performed on text data augmentation compared to video data . |
| Approach: | They propose three methods to augment sign language text data using Korean sign language gloss dictionary. |
| Outcome: | The proposed method improves translation performance by 0.204 and 0.170 compared to the original data. |
Minimal Yet Big Impact: How AI Agent Back-channeling Enhances Conversational Engagement through Conversation Persistence and Context Richness (2024.findings-emnlp)
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| Challenge: | Increasing use of AI agents in conversational services highlights the importance of back-channeling (BC) as an active listening strategy to enhance conversational engagement. |
| Approach: | They conducted an experiment with 55 participants to evaluate conversational engagement using both quantitative and qualitative metrics. |
| Outcome: | The results show that the Todak_BC and TodAK_NoBC groups have significantly higher conversational engagement than the Todask_NoB. |
BPM_MT: Enhanced Backchannel Prediction Model using Multi-Task Learning (2021.emnlp-main)
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| Challenge: | Backchannel (BC) is a short and quick reaction signal of a listener to a speaker's utterances. |
| Approach: | They propose a model that utilizes lexical information in utterances to enhance backchannel (BC) prediction. |
| Outcome: | The proposed model showed 14.24% performance improvement compared to baseline in the four BC categories: continuer, understanding, empathic response, and No BC. |
A Model of Cross-Lingual Knowledge-Grounded Response Generation for Open-Domain Dialogue Systems (2021.findings-emnlp)
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| Challenge: | Existing studies on open-domain dialogue systems that allow free topics are challenging . however, non-English dialogue systems suffer from reproducing the performance of English dialogue systems . |
| Approach: | They propose to use English knowledge to improve the performance of open-domain dialogue systems . they construct a Korean-English T5 language model and develop a knowledge-grounded Korean dialogue model . |
| Outcome: | The proposed model improves even when only English knowledge is given . the model is built with a pre-trained language model and a knowledge-grounded Korean dialogue model . |