Papers with compounding error

2 papers
Straight to the Tree: Constituency Parsing with Neural Syntactic Distance (P18-1)

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Challenge: Compared to traditional shift-reduce parsing schemes, our approach is free from the potentially disastrous compounding error.
Approach: They propose a model that predicts a scalar for each split position in a sentence and then determines the topology of grammar tree based on syntactic distances.
Outcome: The proposed model achieves the state-of-the-art single model F1 score of 92.1 on PTB and 86.4 on CTB dataset, surpassing the previous single model results by a large margin.
Tool learning via Inference-time Scaling and Cycle Verifier (2025.findings-acl)

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Challenge: In inference-time scaling, Chain-of-Thought (CoT) data is scarce or even unavailable.
Approach: They propose a method which establishes an inference cycle to synthesize user queries and CoT data.
Outcome: The proposed method achieves a 75.4% pass rate and a 79.6% win rate using small models in StableToolBench.

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