Papers by Minh-Quoc Nghiem
APLenty: annotation tool for creating high-quality datasets using active and proactive learning (D18-2)
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| Challenge: | APLenty is an annotation tool for creating high-quality sequence labeling datasets using active and proactive learning. |
| Approach: | They present APLenty, an annotation tool for creating high-quality sequence labeling datasets using active and proactive learning. |
| Outcome: | The proposed tool is highly flexible and can be adapted to various other tasks. |
Text Classification and Prediction in the Legal Domain (2022.lrec-1)
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| Challenge: | a case study combines text classification and legal judgment prediction for flight compensation . a human-in-the-loop model outperformed human prediction when predicting a claim being successful . |
| Approach: | They combine transformer-based classification models with human-in-the-loop data to classify airlines' responses to flight compensation claims. |
| Outcome: | The proposed models outperform human prediction when predicting a legal claim success. |
Paladin: an annotation tool based on active and proactive learning (2021.eacl-demos)
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| Challenge: | Existing tools for active learning focus on the active learning algorithms and provide no user interface thus making it difficult to use for the end-users. |
| Approach: | They present an open-source web-based annotation tool for creating high-quality multi-label document-level datasets that integrates active learning and proactive learning. |
| Outcome: | The proposed tool is designed for multi-label annotation, but it can be adapted to other tasks in single-l Label settings. |