POS Tagging Model With Joint Spacing Error Compensation
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Solution Overview
Problem
Natural language processing systems face performance deterioration due to spacing errors in morpheme analysis and part-of-speech (POS) tagging, leading to error propagation in tasks such as intent identification and dialogue systems.
Innovation Solution
A POS tagging model is trained to simultaneously perform morpheme analysis and spacing by using a pre-trained language model and bidirectional simple recurrent units (SRU) encoders, along with conditional random field (CRF) discriminators, to calculate loss values and adjust parameters based on total loss, enhancing robustness to spacing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional morpheme analysis and POS tagging are performed separately, then the processing pipeline is simple, but performance deteriorates when spacing errors occur in the input sentence
Solution Approach 1:
The patent merges morpheme analysis and spacing prediction into a single unified model. The encoder processes the input sentence once and simultaneously generates both morpheme analysis results and spacing predictions, eliminating the need for separate processing pipelines and improving robustness to spacing errors through joint optimization
Solution Approach 2:
The model is designed with multi-functionality to perform both morpheme analysis and spacing prediction using a single encoder architecture. The encoder serves multiple purposes: extracting morphological information, predicting spacing boundaries, and generating POS tags, thereby reducing dependency on perfectly spaced input
2Reliability
If spacing errors are present in the input sentence, then the input processing is faster, but error propagation occurs in subsequent tasks
Solution Approach 1:
The model performs preliminary spacing prediction during the encoding process itself, rather than requiring perfect spacing as a prerequisite. By predicting spacing boundaries as part of the encoding output, the system compensates for potential spacing errors before they can propagate to subsequent processing tasks
3Reliability
If morpheme analysis and POS tagging are performed independently, then the implementation is simpler, but the system cannot compensate for spacing errors
Solution Approach 1:
The training process combines morpheme analysis objectives and spacing prediction objectives into a single unified training framework. The encoder is trained to simultaneously optimize for both morphological correctness and spacing accuracy, allowing the system to compensate for spacing errors through the joint learning of both functions
Data Source
AI summary
A method of training a part-of-speech (POS) tagging model includes: separating an input sentence into units of syllables to generate an input sequence; encoding, using at least one encoder included in a part-of-speech (POS) tagging model, the input sequence; generating, based on the encoded input sequence and using a first discriminator included in the POS tagging model, a POS tagging result; and generating, based on the encoded input sequence and using a second discriminator included in the POS tagging model, a spacing result.