Papers by Junseok Kim
Self-Training using Rules of Grammar for Few-Shot NLU (2021.findings-emnlp)
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| Challenge: | Existing methods for learning natural language understanding are limited in low-resource settings. |
| Approach: | They propose to use rules of grammar to construct and expand rules of grammatical structure of data without human involvement. |
| Outcome: | The proposed approach outperforms state-of-the-art methods in three benchmark datasets. |
Persona Switch: Mixing Distinct Perspectives in Decoding Time (2026.findings-eacl)
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| Challenge: | Existing studies show that role-play prompting improves zero-shot reasoning, but these improvements are inconsistent across tasks and instances. |
| Approach: | They propose a method that dynamically combines the benefits of both prompting strategies. |
| Outcome: | The proposed method outperforms baselines and shows that output confidence is an important measure for selecting the more reliable output. |
Reliability-Aware Adaptive Self-Consistency for Efficient Sampling in LLM Reasoning (2026.findings-acl)
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| Challenge: | Self-consistency improves reasoning reliability but incurs substantial inference cost . Adaptive self-consistent methods rely on count-based stopping rules that treat all responses equally . |
| Approach: | They propose a method that reframs adaptive sampling from response counting to evidence sufficiency by leveraging response-level confidence. |
| Outcome: | The proposed method reduces inference cost by up to 70% while preserving accuracy on GSM8K. |
KNU-HYUNDAI’s NMT system for Scientific Paper and Patent Tasks onWAT 2019 (D19-52)
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Cheoneum Park, Young-Jun Jung, Kihoon Kim, Geonyeong Kim, Jae-Won Jeon, Seongmin Lee, Junseok Kim, Changki Lee
| Challenge: | We submitted our transformer-based neural machine translation system to the translation tasks of the 6th workshop on Asian Translation (WAT 2019). |
| Approach: | They propose a transformer-based neural machine translation system for Chinese-Japanese, English-Japanese, and Korean->Japanoise translation tasks. |
| Outcome: | The proposed system performed well on the two translation tasks and was ranked first in terms of the BLEU scores in all the JPC2 subtasks. |
SEAM: Bridging the Temporal-Semantic Granularity Gap for LLM-based Speech Recognition (2026.findings-eacl)
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| Challenge: | Existing duration-based methods generate embeddings at fixed rates, creating distributional mismatch with LLM pre-training. |
| Approach: | They propose an encoder-decoder architecture that generates embeddings at variable rates through cross-attention between speech features and text embeddables. |
| Outcome: | The proposed architecture achieves competitive performance on LibriSpeech (2.6%/5.2% WER) and 4.7% WER on TED-LIUM-v2 with a multi-stage training strategy and First Token Guidance. |
Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer (2021.findings-emnlp)
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| Challenge: | Visual dialog is a task of answering questions grounded in an image using dialog history as context. |
| Approach: | They propose a Sparse Graph Learning method to formulate visual dialog as a graph structure learning task. |
| Outcome: | The proposed model outperforms the state-of-the-art models on the VisDial v1.0 dataset. |