RNN Autoencoder Translation Intermediary for Sentence Feature Extraction
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Solution Overview
Problem
Natural language processing techniques, specifically RNN autoencoders, face challenges in accurately capturing the feature quantities of sentences due to ambiguity in language expressions, leading to errors in sentence classification and search tasks, as they struggle to differentiate between sentences with similar meanings expressed differently.
Innovation Solution
A learning program and apparatus that utilize machine learning to receive and translate sentences between languages, perform morphological analysis, and train conversion parameters using RNN autoencoders, allowing for the reduction of ambiguity influence by setting and optimizing conversion parameters to accurately represent sentence meanings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If RNN autoencoder is used for sentence feature extraction, then sentence representation can be obtained, but accuracy deteriorates due to language expression ambiguity
Solution Approach 1:
The patent introduces a translation system as an intermediary component between the RNN autoencoder and the sentence representation extraction. The translation system converts sentences into a standardized target language form, which then serves as input to the RNN autoencoder. This intermediary step reduces the impact of language expression ambiguity by normalizing diverse linguistic expressions into a consistent format, thereby improving both the measurement precision of feature extraction and the reliability of subsequent classification and search tasks
2Measurement precision
If translation is performed to reduce ambiguity, then sentence representation accuracy improves, but processing complexity increases
Solution Approach 1:
The patent merges the translation function and the RNN autoencoder feature extraction function into an integrated processing system. The translation module and the neural network model are combined in a unified architecture where the translation output directly feeds into the autoencoder input. This merging reduces overall system complexity by eliminating separate processing stages and enabling end-to-end training, while still achieving improved sentence meaning representation accuracy through the translation step
Data Source
AI summary
A non-transitory computer-readable recording medium having stored therein a learning program for causing a computer to execute a process includes: receiving a first sentence expressed in a first language and a second sentence obtained by translating the first sentence; and training a conversion parameters configured to convert each word of a received first sentence into a corresponding word of a received second sentence by utilizing machine learning.