Neural Dependency Parsing Without POS Tags
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Solution Overview
Problem
Current natural language processing systems face challenges in accurately understanding spoken commands due to cascading POS tagging errors and resource-intensive computational requirements, leading to unreliable and slow performance in generating dependency parses for text segments.
Innovation Solution
A neural network-based system that generates dependency parses without directly using POS tags, reusing representations from initial neural network layers, and employing a continuous stacking method for efficient backpropagation across POS tagging and dependency parsing tasks, reducing the model size and improving prediction accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If POS tags are directly used in generating dependency parse, then the dependency parse can be generated with explicit grammatical information, but cascading POS tagging errors reduce prediction accuracy
Solution Approach 1:
The patent extracts and removes the POS tagging step from the traditional pipeline that feeds into dependency parsing. By not directly using POS tags as input features for the dependency parser, the system eliminates the propagation of POS tagging errors while still achieving accurate dependency parse generation through direct neural network processing of the input sequence.
Solution Approach 2:
The patent segments the natural language processing task into independent neural network components: an initial neural network for feature extraction and a separate dependency parsing neural network. This segmentation allows each component to be optimized independently and prevents error cascading between sequential processing stages.
2Measurement precision
If lexicalized features are used by the dependency parser, then more detailed linguistic information is available, but the model size increases and processing speed decreases
Solution Approach 1:
The patent creates universal neural network representations that serve multiple functions: they capture detailed linguistic information needed for accurate dependency parsing while maintaining computational efficiency. The shared representations are used by both the initial neural network and the dependency parsing network, eliminating the need for separate lexicalized feature processing.
Solution Approach 2:
The patent changes the parameter representation from traditional lexicalized features to continuous neural network embeddings. This parameter transformation maintains the rich linguistic information while enabling faster computational processing through efficient vector operations and gradient-based optimization.
3Ease of manufacture
If traditional cascaded pipeline is used for POS tagging and dependency parsing, then each task can be processed separately, but training efficiency and speed are reduced
Solution Approach 1:
The patent merges the POS tagging and dependency parsing tasks into a unified neural network framework with shared initial layers. This combining allows simultaneous training of both tasks through backpropagation, improving training efficiency and speed while maintaining the ability to perform both functions.
Solution Approach 2:
The patent implements feedback mechanisms through backpropagation across the entire neural network pipeline. Error signals from both POS tagging and dependency parsing objectives are propagated back through the shared initial layers, enabling joint optimization of both tasks and improving overall training efficiency.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating dependency parses for input text segments, which may be provided as inputs to a natural language processing system. One of the systems includes a first neural network comprising: one or more initial neural network layers configured to, for each token in an input text sequence: receive features for the token; and collectively process the features to generate an alternative representation of the features for use in determining a part of speech of the token in the input text sequence; and a dependency parsing neural network configured to: process the alternative representations of the features for the tokens in the input text sequence generated by the one or more initial neural network layers to generate a dependency parse of the input text sequence.