Synthetic Treebank Generation for Low-Resource Parser Training
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Solution Overview
Problem
Production systems face challenges in deploying accurate parsers due to the computational complexity of generating high-quality treebanks, especially for lesser-used languages or domains, where limited human-annotated data is available, and neural network-based parsers are slow and resource-intensive.
Innovation Solution
A method using a transformer enhanced parser neural network model to generate synthetic treebanks automatically, combining a transformer model with a parser model to create contextualized embeddings, allowing for the training of parsers in production systems without human supervision, even in scenarios with limited training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human-annotated treebanks are used to train parsers, then parsing accuracy is improved, but the cost and time for data annotation increase significantly
Solution Approach 1:
The patent uses a pre-trained transformer parser to generate synthetic treebank data that copies the structural patterns and linguistic knowledge from the pre-trained model. This synthetic data serves as a substitute for expensive human-annotated treebanks, enabling training of parsers for low-resource languages without requiring manual annotation while maintaining parsing accuracy through the transfer of linguistic patterns from the pre-trained model
Solution Approach 2:
The patent performs preliminary training of a transformer parser on high-resource languages to pre-learn linguistic patterns and syntax rules. This pre-trained model then generates synthetic treebank data that encapsulates these preliminary learned patterns, which can be used to train parsers for low-resource languages, eliminating the need for time-consuming annotation while preserving parsing accuracy through transfer learning
2Measurement precision
If neural network-based parsers are deployed in production systems, then parsing accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The patent generates synthetic treebank data using a pre-trained transformer parser, creating a lightweight training dataset that can be used to train simpler, more efficient parser models for production deployment. This approach copies the essential linguistic knowledge into the training data itself, allowing the use of less computationally intensive models while maintaining accuracy
Solution Approach 2:
The patent creates synthetic treebank data that serves as a disposable training resource - once generated, this synthetic data can be used to train multiple parser instances without requiring ongoing computational resources. The synthetic treebank acts as a cheap, reusable training artifact that eliminates the need for continuous access to expensive pre-trained models or large amounts of human-annotated data
3Adaptability or versatility
If parsers are trained for lesser-used languages, then language coverage is improved, but the availability of training data worsens
Solution Approach 1:
The patent uses a pre-trained transformer parser to copy linguistic patterns and syntax rules from high-resource languages into synthetic treebank data for low-resource languages. This copying mechanism transfers universal linguistic knowledge across language boundaries, enabling the creation of training data for lesser-used languages without requiring actual annotated examples from those languages, thus improving language coverage despite data scarcity
Solution Approach 2:
The patent employs a universal transformer parser pre-trained on multiple high-resource languages that learns language-agnostic linguistic patterns. This universal model can generate synthetic treebank data for any target language by applying the same linguistic rules, making the system universally applicable across languages regardless of data availability and enabling extended language coverage through multi-functional transfer learning
Data Source
AI summary
An approach for generating synthetic treebanks to be used in training a parser in a production system is provided. A processor receives a request to generate one or more synthetic treebanks from a production system, wherein the request indicates a language for the one or more synthetic treebanks. A processor retrieves at least one corpus of text in which the requested language is present. A processor provides the at least one corpus to a transformer enhanced parser neural network model. A processor generates at least one synthetic treebank associated with a string of text from the at least one corpus of text in which the requested language is present. A processor sends the at least one synthetic treebank to the production system, wherein the production system trains a parser utilized by the production system with the at least one synthetic treebank.