Delexicalized Parser Transfer for Low-Resource Language Parsing
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Solution Overview
Problem
Dependency parsers face challenges in languages lacking labeled training data, as existing methods rely heavily on gold standard data for accurate parsing, which is not available for all languages.
Innovation Solution
A delexicalized parser trained on source-language data is used to generate parses for a target language, with part-of-speech tags being used to align with the source-language parse, allowing for the update of parameter vectors and enabling parsing without relying on parallel corpora, even in languages with no syntactic resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a dependency parser is trained using gold standard labeled training data, then parsing accuracy is improved, but the method cannot be applied to languages lacking labeled data
Solution Approach 1:
The patent copies the delexicalized parser model trained on source language gold standard data and applies it to target languages without requiring target language labeled data. The model structure and training approach are replicated across multiple languages through direct transfer, enabling parsing accuracy in languages previously inaccessible to supervised training methods.
Solution Approach 2:
The patent changes the parameter representation by using delexicalized features (removing word-specific information) that can be universally applied across languages. By transforming the input representation to focus on syntactic roles rather than lexical items, the model parameters become language-agnostic and can be transferred to any language with appropriate tokenization and POS tagging.
2Reliability
If a lexicalized parser is used for target language parsing, then parsing performance is improved, but it requires parallel corpora and syntactic resources that are not available for all languages
Solution Approach 1:
The patent extracts and removes lexical information from the parsing model, creating a delexicalized version that operates on syntactic structures alone. By taking out language-specific lexical dependencies, the model no longer requires parallel corpora or target language syntactic resources, reducing data requirements while maintaining parsing performance through transfer learning.
Solution Approach 2:
The delexicalized parser achieves universality by being trained on source language data and then applied to multiple target languages without retraining. The model performs the function of parsing across different languages using the same trained parameters, eliminating the need for language-specific adaptations or additional syntactic resources for each target language.
3Adaptability or versatility
If delexicalized transfer is used for languages with no labeled data, then parsing becomes possible without gold standard data, but accuracy may be reduced compared to fully supervised approaches
Solution Approach 1:
The patent performs preliminary training on source language gold standard data to establish a robust delexicalized parser model before transferring it to target languages. This preliminary action on richly annotated data creates a strong foundation that compensates for the lack of target language labeled data, maintaining accuracy while enabling coverage of previously inaccessible languages.
Solution Approach 2:
The delexicalized parser acts as an intermediary between source language gold standard data and target language parsing tasks. It mediates the transfer of syntactic knowledge by removing lexical specifics while preserving structural patterns, allowing accurate parsing in target languages without direct access to their labeled training data.
Data Source
AI summary
A source language sentence is tagged with non-lexical tags, such as part-of-speech tags and is parsed using a lexicalized parser trained in the source language. A target language sentence that is a translation of the source language sentence is tagged with non-lexical labels (e.g., part-of speech tags) and is parsed using a delexicalized parser that has been trained in the source language to produce k-best parses. The best parse is selected based on the parse's alignment with lexicalized parse of the source language sentence. The selected best parse can be used to update the parameter vector of a lexicalized parser for the target language.