Papers with easy-first strategy

2 papers
Multiple Tasks Integration: Tagging, Syntactic and Semantic Parsing as a Single Task (2021.eacl-main)

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Challenge: Existing systems that bypass intermediate levels of analysis are prone to error propagation and are therefore free from interference.
Approach: They propose a multitask paradigm orthogonal to weight sharing that uses multiple tasks to process input iteratively but concurrently at multiple levels of analysis.
Outcome: The proposed model uses reinforcement learning and release from sequential constraints to improve the quality of the syntactic and semantic parses.
Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies (P19-1)

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Challenge: Existing dependency parsing algorithms do not support directed acyclic graphs . a a systole-based dependency parses sentences using binary semantic relations that are not trees .
Approach: They propose an iterative predicate selection algorithm for semantic dependency parsing . they train the algorithm using multi-task learning and task-specific policy gradient training .
Outcome: The proposed model achieves a new state of the art on the SemEval 2015 task 18 dataset .

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