Papers by Dongkyu Lee
MATTER: Memory-Augmented Transformer Using Heterogeneous Knowledge Sources (2024.findings-acl)
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| Challenge: | Existing retrieval-augmented models typically retrieve information from a single type of knowledge source. |
| Approach: | They propose an efficient memory-augmented transformer to retrieve relevant knowledge from multiple knowledge sources. |
| Outcome: | The proposed model outperforms existing retrieval-augmented models on popular QA benchmarks in terms of accuracy and speed. |
Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation (2022.acl-long)
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Yingxiu Zhao, Zhiliang Tian, Huaxiu Yao, Yinhe Zheng, Dongkyu Lee, Yiping Song, Jian Sun, Nevin Zhang
| Challenge: | Building models of natural language processing (NLP) is challenging in low-resource scenarios where limited data are available. |
| Approach: | They propose a memory imitation meta-learning method that enhances the model’s reliance on support sets for task adaptation. |
| Outcome: | The proposed method outperforms baselines on both text classification and generation tasks. |
Semi-Supervised Lifelong Language Learning (2022.findings-emnlp)
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Yingxiu Zhao, Yinhe Zheng, Bowen Yu, Zhiliang Tian, Dongkyu Lee, Jian Sun, Yongbin Li, Nevin L. Zhang
| Challenge: | Existing methods to learn languages only focus on supervised learning, and unlabeled data is underexplored. |
| Approach: | They propose a semi-supervised lifelong language learning setting where a model learns sequentially arriving language tasks with both labeled and unlabeled data. |
| Outcome: | The proposed model outperforms baseline models on various language tasks and is effective and superior to existing models. |
Local Temperature Beam Search: Avoid Neural Text DeGeneration via Enhanced Calibration (2023.findings-acl)
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Dongkyu Lee, Gyeonghun Kim, Janghoon Han, Taesuk Hong, Yi-Reun Kim, Stanley Jungkyu Choi, Nevin L. Zhang
| Challenge: | Existing approaches to inference have been based on stochastic decoding but they sacrifice output quality due to randomness. |
| Approach: | They propose a deterministic decoding scheme, local temperature beam search, which reduces repetition while maintaining the level of coherence as in beam search. |
| Outcome: | The proposed inference scheme reduces repetition while maintaining coherence as in beam search. |
Adaptive Label Smoothing with Self-Knowledge in Natural Language Generation (2022.emnlp-main)
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| Challenge: | Overconfidence in model generalization and calibration has been shown to impair model generalisation and calibration. |
| Approach: | They propose a regularization scheme that takes model probability into account and takes it into account . they use a prior label distribution to smooth target labels . |
| Outcome: | The proposed model improves model generalization and calibration by taking model probability into account. |
Hard Gate Knowledge Distillation - Leverage Calibration for Robust and Reliable Language Model (2022.emnlp-main)
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| Challenge: | Existing knowledge distillation schemes focus on a teacher as a source of knowledge and a gauge to detect miscalibration of a student. |
| Approach: | They propose a method that uses a teacher model as a source of knowledge and a model as an error detector to detect miscalibration of a student. |
| Outcome: | The proposed scheme improves model generalization and significantly lowers calibration error. |
Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization (2021.acl-long)
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| Challenge: | Text style transfer aims to alter the style of a sentence while preserving its content. |
| Approach: | They propose to remove style information at token level and fuse it to style representations using conditional layer normalization. |
| Outcome: | The proposed model outperforms the state-of-the-art models in terms of content preservation and fluency. |
Empathetic and Emotionally Positive Conversation Systems with an Emotion-specific Query-Response Memory (2022.findings-emnlp)
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Zhiliang Tian, Yinliang Wang, Yiping Song, Chi Zhang, Dongkyu Lee, Yingxiu Zhao, Dongsheng Li, Nevin L. Zhang
| Challenge: | Existing emotional conversation systems output responses according to either a given emotion or the user’s emotion reflected in the input queries. |
| Approach: | They propose to generate empathetic responses catering to the user’s emotions while leading the conversation to be emotionally positive by abstracting the conversation corpus and extracting the different responding strategies for different users’ emotions and conversational topics into a memory. |
| Outcome: | The proposed model surpasses the baseline methods in appropriateness, diversity, and generating emotionally positive responses. |
Response-Anticipated Memory for On-Demand Knowledge Integration in Response Generation (2020.acl-main)
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| Challenge: | Neural conversation models generate appropriate but non-informative responses in general. |
| Approach: | They propose to construct a document memory with anticipated responses in mind using a teacher-student framework and a student's input. |
| Outcome: | The proposed model outperforms the state-of-the-art for the Conversing by Reading task. |