Tag Alignment Module for Structured Text Translation
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Solution Overview
Problem
Current machine translation systems face inefficiencies when translating structured text with embedded tags, as they often slow down the translation process and fail to accurately handle the tags, leading to suboptimal translation quality.
Innovation Solution
A system and method for translating structured text using an attention-based transformer network with a tag alignment module that preprocesses input text by converting complex tags to simple ones, aligns tags during translation, and post-processes the output to restore original tags, ensuring efficient handling of embedded tags and improved translation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If all structured tags are translated, then translation completeness is improved, but translation speed deteriorates
Solution Approach 1:
The patent segments the translation process into three distinct phases: preprocessing (converting complex tags to simple placeholders), translation (translating only the text content), and postprocessing (restoring the original tags). This segmentation allows the system to translate only necessary text while preserving tag structure, thereby improving translation speed without sacrificing completeness.
Solution Approach 2:
The patent extracts and removes structured tags from the text before translation, translating only the pure text content. After translation, the original tags are restored to their appropriate positions. This extraction approach eliminates unnecessary translation overhead while ensuring all text content is accurately translated.
2Measurement precision
If structured tags are translated, then tag accuracy is improved, but translation efficiency deteriorates
Solution Approach 1:
The patent performs preliminary action by converting complex structured tags to simple placeholders before the translation process begins. This preprocessing step ensures tag accuracy is maintained while avoiding the inefficiency of translating tag content, as the tags are restored in their original form during postprocessing.
Solution Approach 2:
The patent introduces simple placeholder tags as intermediaries between the original complex tags and the translation process. These placeholders serve as mediators that preserve tag structure and meaning without requiring translation, thereby maintaining tag accuracy while improving translation efficiency.
3Stability of the object's composition
If complex tags are processed during translation, then tag integrity is improved, but processing time increases
Solution Approach 1:
The patent segments tag processing from text translation by handling tags in preprocessing and postprocessing phases, while translating only text content in the middle phase. This segmentation preserves tag integrity through systematic conversion and restoration, while minimizing processing time by avoiding redundant tag translation operations.
Solution Approach 2:
The patent applies different processing qualities to different parts of the structured text: complex tags are converted to simple placeholders in preprocessing, text content is translated in full, and tags are restored in postprocessing. This local quality approach ensures tag integrity is maintained where needed while optimizing processing time for text translation.
Data Source
AI summary
Approaches for the translation of structured text include an embedding module for encoding and embedding source text in a first language, an encoder for encoding output of the embedding module, a decoder for iteratively decoding output of the encoder based on generated tokens in translated text from previous iterations, a beam module for constraining output of the decoder with respect to possible embedded tags to include in the translated text for a current iteration using a beam search, and a layer for selecting a token to be included in the translated text for the current iteration. The translated text is in a second language different from the first language. In some embodiments, the approach further includes scoring and pointer modules for selecting the token based on the output of the beam module or copied from the source text or reference text from a training pair best matching the source text.