Neural Network Symbol Sequence Generation for Sentence Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing sentence compression methods struggle to preserve important words located deep in a dependency structure tree during the compression process, as they preferentially delete words from leaf nodes, leading to the loss of critical information such as nation names.
Innovation Solution
A symbol sequence generation device employing a pre-trained neural network with an attention mechanism that weights hidden states based on dependency structure probabilities, ensuring that important words are retained by considering the sequence of dependency structures from any given word to its ancestors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If words are deleted successively from the leaf nodes of the dependency structure tree, then the compression process is simple and efficient, but important words located deep in the tree (close to leaf nodes) are preferentially deleted even when they are critical
Solution Approach 1:
The patent applies preliminary action by computing attention weights for all words based on their dependency relationships before the actual compression decision is made. The attention mechanism pre-evaluates the importance of each word by considering its distance to the root and its role in the dependency structure, allowing the system to preserve important deep words before the compression process begins.
Solution Approach 2:
The patent changes the parameter of word importance evaluation by introducing attention weights that are computed based on dependency structure distances. Instead of using a simple leaf-node deletion strategy, the system transforms the evaluation criterion to incorporate the attention mechanism, which dynamically adjusts the importance score of each word based on its positional relationship in the dependency tree.
2Measurement precision
If the dependency structure tree is considered to preserve important deep words, then the accuracy of word selection improves, but the computational complexity increases due to tracking dependency sequences
Solution Approach 1:
The patent applies segmentation by breaking down the complex dependency structure analysis into manageable components. The attention mechanism divides the computation into individual word-level attention calculations, where each word's importance is evaluated independently based on its distance to the root and its dependency relationships, rather than analyzing the entire tree structure at once.
Solution Approach 2:
The patent introduces an intermediary mechanism (the attention weight computation) that mediates between the raw dependency structure and the final compression decision. The attention mechanism serves as an intermediate layer that translates the complex dependency relationships into simplified importance scores, making the subsequent compression process more manageable while preserving accuracy.
Data Source
AI summary
This disclosure relates to a method of generating a symbol string based on an input sentence represented by a sequence of symbols. In particular, the method involves receiving an input symbol string representing a sentence, generating, using a neural network based on a sequence of dependency structure of elements in the input symbol string, an output symbol string corresponding to the input sentence. The neural network includes an encoder that converts elements of the input symbol string to a first hidden state in a form of a multi-dimensional vector, an attention mechanism that applies a weight to the first hidden state and generates the weighted first hidden state as a second hidden state, a decoder that generates a third hidden state based on at least one element of the input symbol string, an element of the output symbol string, and the second hidden state, and an output generator that generates an element of the output symbol string based on the second hidden state and the third hidden state. The output symbol string may represent a sequence of labels to process the input sentence.