Papers by Eunseop Yoon
TLCR: Token-Level Continuous Reward for Fine-grained Reinforcement Learning from Human Feedback (2024.findings-acl)
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Eunseop Yoon, Hee Suk Yoon, SooHwan Eom, Gunsoo Han, Daniel Nam, Daejin Jo, Kyoung-Woon On, Mark Hasegawa-Johnson, Sungwoong Kim, Chang Yoo
| Challenge: | Existing approaches to provide token-level rewards fail to account for varying degrees of preference inherent to each token. |
| Approach: | They propose a reward model that uses a discriminator to assign token-based continuous rewards to each token considering the context. |
| Outcome: | Extensive experiments show that the proposed reward model improves on open-ended language generation benchmarks. |
Information-Theoretic Text Hallucination Reduction for Video-grounded Dialogue (2022.emnlp-main)
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| Challenge: | Existing video-grounded dialogue systems suffer from text hallucination problem due to learning spurious correlations from the fact that answer sentences in the dataset usually include the words of input texts. |
| Approach: | They propose to decode an answer sentence to a question using video and dialogue contexts. |
| Outcome: | The proposed framework shows that it generates adequate conversational responses to the queries of humans while following up on video and dialogue context. |
HEAR: Hearing Enhanced Audio Response for Video-grounded Dialogue (2023.findings-emnlp)
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| Challenge: | Existing systems are competent only to incorporate information in the video and text and tend to struggle in extracting the necessary information from the audio when generating appropriate responses to the question. |
| Approach: | They propose to perform sensible listening by selectively attending to audio whenever the question requires it. |
| Outcome: | The proposed framework enhances the accuracy and audibility of VGD systems in a model-agnostic manner. |
Efficient Latent Variable Modeling for Knowledge-Grounded Dialogue Generation (2023.findings-emnlp)
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Gunsoo Han, Daejin Jo, Daniel Nam, Eunseop Yoon, Taehwan Kwon, Seungeun Rho, Kyoung-Woon On, Chang Yoo, Sungwoong Kim
| Challenge: | Existing knowledge-grounded dialogue generation algorithms require annotated knowledge to generate a response grounded on the retrieved knowledge. |
| Approach: | They propose an efficient algorithm for latent variable modeling that leverages large amount of dialogue data. |
| Outcome: | The proposed algorithm outperforms the supervised learning algorithm on knowledge-grounded dialogue datasets while maintaining efficiency and scalability. |
SMSMix: Sense-Maintained Sentence Mixup for Word Sense Disambiguation (2022.findings-emnlp)
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| Challenge: | Word Sense Disambiguation (WSD) is an NLP task aimed at determining the correct sense of a word in a sentence from discrete sense choices. |
| Approach: | They propose a word-level mixup method that maintains the sense of a target word by increasing the frequency of least frequent senses during training to reduce the distributional bias of senses. |
| Outcome: | The proposed method shows high accuracy on the most frequent sense (MFS) and low accuracy on least frequent senses (LFS). |
INTapt: Information-Theoretic Adversarial Prompt Tuning for Enhanced Non-Native Speech Recognition (2023.findings-acl)
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| Challenge: | Existing approaches to improve ASR performance with pre-trained models require updating the pre-training model weights. |
| Approach: | They propose a method that uses prompts concatenated to the original input to re-modulate attention of the pre-trained model. |
| Outcome: | The proposed model improves the performance of L2 English and increases similarity between L2 and L1 accents. |