Papers with annotation errors
Joint End-to-end Semantic Proto-role Labeling (2023.acl-short)
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| Challenge: | Existing systems for semantic proto-role labeling assign binary properties to arguments based on agent-like or patient-like properties. |
| Approach: | They propose to use a deep transformer model to model the performance of semantic proto-role labeling . they propose to include an error analysis to understand correlations between system stages . |
| Outcome: | The proposed system is robust in the presence of predicted arguments, the authors show . the proposed system also reduces annotation errors, the researchers conclude . |
Linguistically Conditioned Semantic Textual Similarity (2024.acl-long)
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| Challenge: | Semantic textual similarity (STS) is a fundamental NLP task that measures the semantic similarity between two sentences. |
| Approach: | They propose to use a conditional STS dataset to measure sentences’ similarity conditioned on a certain aspect to reduce the inherent ambiguity posed by the sentences. |
| Outcome: | The proposed method improves the performance over baselines on the C-STS dataset with over 80% F1 score. |
EVADE: LLM-Based Explanation Generation and Validation for Error Detection in NLI (2026.findings-acl)
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| Challenge: | Human label variation (HLV) arises when multiple labels are valid for the same instance. |
| Approach: | They propose a framework for generating and validating explanations to detect errors using large language models (LLMs) EVADE framework provides broader explanation coverage and requires less human intervention . |
| Outcome: | The proposed framework provides broader explanation coverage, requires less human intervention, and delivers better downstream performance in predicting label distributions. |
Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards? (2021.acl-long)
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| Challenge: | Rather than replacing leaderboards, we advocate a re-imagining of the model to highlight if and where progress is made. |
| Approach: | They propose a Bayesian leaderboard model where latent subject skill and latent item difficulty predict correct responses. |
| Outcome: | The proposed model can guide what to annotate, identify annotation errors, detect overfitting, and identify informative examples. |
Rationally Reappraising ATIS-based Dialogue Systems (P19-1)
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| Challenge: | Recent state-of-the-art neural models have obtained F1-scores near 98% on the task of slot filling. |
| Approach: | They propose to fix annotation errors in ATIS and propose a rule-based grammar for slot filling that achieves a 95.82% F1 score. |
| Outcome: | The proposed grammar achieves a 95.82% F1-score on the ATIS domain. |