Dependency Parser Joint Optimization for Downstream Tasks
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Solution Overview
Problem
Dependency parsers often output the best parse based on their intrinsic scoring system, which may not be optimal for specific downstream natural language processing tasks, requiring adaptation for task-specific parses to improve performance in applications like translation and specialized domains.
Innovation Solution
The parser is trained using a first dataset to determine intrinsic loss and a second dataset to calculate extrinsic loss, modifying its parameters to favor task-specific parses, such as adjusting probabilities and costs to align with expert ratings, thereby improving parse quality for specific applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the parser is trained to optimize intrinsic parsing accuracy, then the parser produces better grammatical structures, but the parser output may not be optimal for specific downstream tasks
Solution Approach 1:
The training process is segmented into two distinct components: intrinsic loss optimization for grammatical accuracy and extrinsic loss optimization for task-specific performance. This segmentation allows the parser to maintain strong parsing fundamentals while adapting to specific downstream tasks through separate optimization objectives.
Solution Approach 2:
The parser employs parameter changes by modifying parsing parameters based on both intrinsic and extrinsic loss signals. The system dynamically adjusts parameters to balance grammatical correctness with task-specific requirements, enabling the same parser to adapt to different downstream applications.
2Ease of manufacture
If the parser uses a single objective function for training, then the training process is simpler, but the parser cannot simultaneously optimize for both parsing quality and task-specific performance
Solution Approach 1:
The system merges two loss functions (intrinsic and extrinsic) into a unified training objective. This combination allows the parser to simultaneously optimize for both parsing quality and downstream task performance through a single joint optimization process, maintaining training simplicity while improving reliability.
Solution Approach 2:
The dual-objective training framework provides universality by enabling the parser to serve multiple functions: maintaining grammatical accuracy while adapting to various downstream tasks. The same training mechanism works across different applications without requiring task-specific retraining.
3Adaptability or versatility
If the parser is retrained on task-specific data, then the parser improves performance for that specific task, but the parser may lose its ability to handle general parsing accurately
Solution Approach 1:
The parser performs preliminary training on general parsing data to establish strong grammatical foundations before adapting to task-specific requirements. This preliminary action ensures that the parser maintains general parsing accuracy while gaining task-specific capabilities through subsequent extrinsic loss optimization.
Solution Approach 2:
The system uses feedback from both intrinsic and extrinsic loss calculations to guide parameter adjustments. This dual feedback mechanism ensures that task-specific adaptations do not compromise general parsing accuracy, as the intrinsic loss continuously monitors and corrects grammatical correctness.
Data Source
AI summary
A plurality of highest intrinsically-ranked parser outputs are scored using an extrinsic metric that is relevant to one or more downstream applications. The parser can be trained by using an extrinsically highly-ranked parse selected from among the intrinsically highly-ranked parses. The selected parse need not be the highest intrinsically-ranked parse. In this way, a parser can be trained to produce better results for downstream applications.