Papers by Taehee Jung
Cluster-Guided Label Generation in Extreme Multi-Label Classification (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing classification-based models are poorly per-form for tail labels and ignore semantic relations among labels. |
| Approach: | They propose to guide label generation using label cluster information to hierarchically generate lower-level labels. |
| Outcome: | The proposed model outperforms classification and generation baselines on tail labels and improves in four popular XMC benchmarks. |
When Thoughts Meet Facts: Reusable Reasoning for Long-Context LMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent Long-Context Language Models (LCLMs) do not capture how evidence should be connected . a new framework that integrates thought templates into LCLM frameworks is proving useful . |
| Approach: | They propose a framework that iteratively refines reusable reasoning patterns derived from prior problem solving to improve their templates. |
| Outcome: | The proposed framework outperforms baselines on knowledge-intensive multi-hop reasoning benchmarks and practical scenarios without retrieval. |
Earlier Isn’t Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization (D19-1)
Copied to clipboard
| Challenge: | despite recent advances in neural summarization systems, the underlying logic behind the improvements remains unexplored. |
| Approach: | They define three sub-aspects of summarization: position, importance, diversity . position exhibits substantial bias in news articles, but not with academic papers . |
| Outcome: | evaluators found that position bias is not present in academic papers and meeting minutes . elucidation provides useful lessons on analyzing summarization datasets . |
Posterior Calibrated Training on Sentence Classification Tasks (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for posterior calibration have been used to correct poorly calibrated posterior probabilities. |
| Approach: | They propose a posterior calibration procedure that optimizes posterior probability distributions while minimizing calibration errors. |
| Outcome: | The proposed procedure reduces calibration error and improves performance on both objectives. |