Papers by Jaemin Kim
A Hierarchical Latent Structure for Variational Conversation Modeling (N18-1)
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| Challenge: | Variational autoencoders suffer from the notorious degeneration problem, according to a new study . utterance drop regularization is an important feature of the hierarchical RNNs . |
| Approach: | They propose a variational hierarchical conversation RNN framework that exploits latent variables and an utterance drop regularization to exploit latent variable. |
| Outcome: | The proposed model outperforms state-of-the-art models on Cornell Movie Dialog and Ubuntu Dialog Corpus. |
Speculative Verification: Exploiting Information Gain for Speculative Decoding (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are used for many applications but their size and computational cost make inference serving a significant challenge. |
| Approach: | They propose an efficient augmentation to Speculative Decoding (SD) that predicts speculation accuracy and dynamically adapts the verification length to maximize throughput. |
| Outcome: | The proposed model reduces wasted verification on rejected tokens and improves decoding efficiency. |
SentiCSE: A Sentiment-aware Contrastive Sentence Embedding Framework with Sentiment-guided Textual Similarity (2024.lrec-main)
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| Challenge: | Sentiment-aware pre-trained language models (PLMs) demonstrate impressive results in downstream sentiment analysis tasks, but they neglect to evaluate the quality of constructed sentiment representations. |
| Approach: | They propose a new metric for evaluating the quality of sentiment representations that is based on the degree of equivalence in sentiment polarity between two sentences. |
| Outcome: | The proposed framework outperforms the existing sentiment-aware models in sentiment analysis tasks. |
A Two-Step Approach for Data-Efficient French Pronunciation Learning (2024.emnlp-main)
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| Challenge: | Recent studies have addressed intricate phonological phenomena in French, relying on extensive linguistic knowledge or a significant amount of sentence-level pronunciation data. |
| Approach: | They propose a grapheme-to-phoneme and post-lexical processing approach to address French phonological phenomena using sentence-level pronunciation data. |
| Outcome: | The proposed approach mitigates the lack of extensive labeled data and serves as a feasible solution for addressing French phonological phenomena even under resource-constrained environments. |
‘Hello, World!’: Making GNNs Talk with LLMs (2025.findings-emnlp)
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| Challenge: | graph neural networks have shown remarkable performance across diverse graph-related tasks, but their high-dimensional hidden representations render them black boxes. |
| Approach: | They propose a graph-based neural network with hidden representations in the form of human-readable text. |
| Outcome: | The proposed GNN outperforms existing LLM-based baseline methods on node classification and link prediction. |