Papers by Danlu Chen
GUARD: Glocal Uncertainty-Aware Robust Decoding for Effective and Efficient Open-Ended Text Generation (2025.findings-emnlp)
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Yuanhao Ding, Esteban Garces Arias, Meimingwei Li, Julian Rodemann, Matthias Aßenmacher, Danlu Chen, Gaojuan Fan, Christian Heumann, Chongsheng Zhang
| Challenge: | GUARD is a self-adaptive decoding method that balances coherence with diversity in open-ended text generation. |
| Approach: | They propose a self-adaptive decoding method that balances coherence and diversity . they combine global entropy estimates with local entropic deviations to integrate uncertainty . |
| Outcome: | GUARD achieves a good balance between diversity and coherence while exhibiting significant improvements in generation speed. |
Translation or Recitation? Calibrating Evaluation Scores for Machine Translation of Extremely Low-Resource Languages (2026.acl-short)
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| Challenge: | Existing studies show that performance across low-resource settings is variable, resulting in a significant barrier for the MT community. |
| Approach: | They propose to use FRED Difficulty Metrics to contextualize reported performance across different language pairs to determine whether breakthroughs reported in other contexts are artifacts of benchmark collection. |
| Outcome: | The proposed metrics explain a significant portion of result variability rather than model capability. |
VizSeq: a visual analysis toolkit for text generation tasks (D19-3)
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| Challenge: | Several softwares for text evaluation are available that do not provide detailed examples. |
| Approach: | They propose a visual analysis toolkit for instance-level and corpus-level system evaluation on a wide variety of text generation tasks. |
| Outcome: | The proposed toolkit covers most common n-gram metrics and latest embedding-based metrics such as BERTScore. |
LogogramNLP: Comparing Visual and Textual Representations of Ancient Logographic Writing Systems for NLP (2024.acl-long)
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| Challenge: | Existing pipelines for natural language processing only process symbolic representations of language, which are labor-intensive and noisy . a large portion of logographic data persists in a purely visual form due to the absence of transcription . this issue poses a bottleneck for researchers seeking to apply NLP to ancient logographic languages . |
| Approach: | They propose a benchmark for NLP analysis of ancient logographic languages using visual representations of writing. |
| Outcome: | The proposed pipeline outperforms existing pipelines for some tasks . the results could unlock large amounts of cultural heritage data of ancient logographic languages . |