Papers with annotation efficiency
Incorporating Chinese Characters of Words for Lexical Sememe Prediction (P18-1)
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| Challenge: | Existing methods of lexical sememe prediction rely on external context information of words to represent meaning. |
| Approach: | They propose a character-enhanced sememe prediction framework for Chinese language that takes advantage of internal character information and external context information. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on a Chinese sememe knowledge base and maintains robust performance even for low-frequency words. |
HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations (2021.acl-long)
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| Challenge: | Existing work on detecting user disengagement requires hand-labeling many dialog samples . Currently, self-reported user ratings are constrained to a static corpus . |
| Approach: | They propose an efficient annotation framework that denoises dialog samples instead of manually labeling them. |
| Outcome: | The proposed framework improves annotation efficiency significantly and detects user disengagement in two dialog corpora. |
D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias (2023.findings-acl)
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| Challenge: | Infusing clustering with active learning with AL can overcome the bias issue of both AL and traditional annotation methods while exploiting AL’s annotation efficiency. |
| Approach: | They propose an algorithm that dynamically adjusts clustering and annotation efforts in response to an estimated classifier error-rate. |
| Outcome: | The proposed algorithm outperforms baseline AL approaches with pretrained transformers and traditional Support Vector Machines on eight datasets for emotion, hatespeech, dialog act, and book type detection tasks. |
Active Learning for Natural Language Generation (2023.emnlp-main)
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| Challenge: | Existing approaches to NLG are limited by the lack of annotated data. |
| Approach: | They propose to use active learning to reduce the cost of manual annotation to improve annotation efficiency by selecting the most informative examples to label. |
| Outcome: | The proposed approach surpasses baseline of random example selection in some cases but not in others. |
KazQAD: Kazakh Open-Domain Question Answering Dataset (2024.lrec-main)
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| Challenge: | KazQAD contains just under 6,000 unique questions with extracted short answers and nearly 12,000 passage-level relevance judgements. |
| Approach: | They introduce a Kazakh open-domain question answering dataset that can be used in reading comprehension and full ODQA settings. |
| Outcome: | The proposed dataset can be used in reading comprehension and full ODQA settings, as well as for information retrieval experiments. |
Rebalancing Label Distribution While Eliminating Inherent Waiting Time in Multi Label Active Learning Applied to Transformers (2024.lrec-main)
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| Challenge: | Data annotation is a resourceintensive endeavor, necessitating human involvement and expertise. |
| Approach: | They propose to annotate instances to rebalance label distribution by judiciously selecting and limiting the data to be annotated. |
| Outcome: | The proposed method mitigates biases, improves model performance and reduces strategy-dependent disparities. |