Papers with surface matching

3 papers
2*n is better than n2: Decomposing Event Coreference Resolution into Two Tractable Problems (2023.findings-acl)

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Challenge: Existing methods for training coreference systems sample from a largely skewed distribution, making it difficult to learn coreference beyond surface matching.
Approach: They propose a heuristic to efficiently filter out a large number of non-coreferent pairs and a training approach on a balanced set of coreferent and non- coreferente mention pairs.
Outcome: The proposed approach significantly reduces compute requirements on two popular ECR datasets while reducing the computational complexity.
Distant Learning for Entity Linking with Automatic Noise Detection (P19-1)

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Challenge: Accurate entity linkers have been produced for domains and languages where no or very limited amounts of labeled data are available.
Approach: They propose to use annotated text to learn to link entities without labeling . they frame the task as a multi-instance learning problem and rely on surface matching to create initial noisy labels.
Outcome: The proposed method outperforms the baseline surface matching model for a subset of entities.
Compartmentalised Agentic Reasoning for Clinical NLI (2026.findings-acl)

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Challenge: Large language models produce fluent judgments for clinical natural language inference, yet fail when the decision requires the correct inferential schema rather than surface matching.
Approach: They propose a compartmentalised agentic framework that routes each premise–statement pair to a reasoning family and applies a specialised solver with explicit verification and targeted refinement.
Outcome: The proposed framework improves mean accuracy from 23% with direct prompting to 57%, with the largest gains on structurally demanding reasoning types.

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