Domain Adaptive Semantic Role Labeler for Sparse Data
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Solution Overview
Problem
Semantic role labeling (SRL) systems perform poorly in sparse data domains due to the lack of training data and inadequate generalization to new domains with shallow semantic information.
Innovation Solution
A domain adaptive approach that assigns weights to data instances based on feature similarities, selects a subset for expert annotation, and combines calibrated source domain data with labeled target domain data to train a machine learning algorithm, optimizing the relevance and coverage of training data for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional SRL models are trained on limited target domain data, then training speed is fast, but model accuracy and generalization performance deteriorate due to sparse data
Solution Approach 1:
The patent combines source domain data and target domain data into a unified training dataset, merging their respective advantages. Source domain data provides volume and coverage while target domain data provides domain-specific accuracy, resolving the contradiction between data quantity and model reliability.
Solution Approach 2:
The patent applies domain adaptation techniques to assign different weights and transformations to source domain data versus target domain data. This local quality approach ensures that each data source contributes optimally to the specific needs of the SRL model for the target domain.
2Reliability
If extensive target domain data is collected and labeled, then model accuracy improves, but time and resource costs increase significantly
Solution Approach 1:
The patent performs preliminary domain adaptation processing on source domain data before combining it with target domain data. This preliminary action prepares the source data to be more useful, reducing the amount of target domain data that needs to be collected and labeled manually.
Solution Approach 2:
The patent uses domain adaptation as an intermediary process that bridges source domain data and target domain data. This intermediary transformation allows source domain data to contribute meaningfully to target domain SRL tasks without requiring extensive manual annotation of target domain data.
3Quantity of substance
If source domain data is used directly for training, then data volume increases, but model performance deteriorates due to domain mismatch
Solution Approach 1:
The patent applies domain adaptation transformations that change the parameters and characteristics of source domain data to better match the target domain. This parameter transformation allows source domain data to maintain its volume advantage while improving domain adaptability.
Solution Approach 2:
Domain adaptation serves as an intermediary process that transforms source domain data into a form that is more compatible with the target domain, resolving the contradiction between data volume and domain adaptability.
4Manufacturing precision
If manual annotation of all target domain data is performed, then data quality is high, but processing cost and time increase
Solution Approach 1:
The patent merges automatically processed source domain data with manually annotated target domain data, creating a hybrid training dataset that maintains high quality while improving processing efficiency through the contribution of unannotated source data.
Solution Approach 2:
The patent applies partial manual annotation to only the most critical target domain data, while relying on domain-adapted source domain data to provide the remaining training signals. This partial action approach maintains sufficient annotation quality while dramatically improving processing efficiency.
Data Source
AI summary
A computer-implemented method according to one embodiment includes identifying features of a plurality of data instances within a target domain; assigning weights to the plurality of data instances within the target domain, based on similarities among the features; selecting a subset of the plurality of data instances within the target domain, based on the weights; associating expert annotations with respective ones of data instances within the subset; and training a machine learning algorithm, utilizing the subset of the plurality of data instances and associated expert annotations.