Document Tag Alignment via Statistical Models
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Solution Overview
Problem
Manual translation of documents with tags often results in incorrect formatting or missing characteristics due to human translators' lack of understanding of tags, leading to inaccurate translations.
Innovation Solution
A computer-implemented technique that uses a hidden Markov model and statistical alignment models to generate alignments between the original document and its manual translation, selects the best alignment based on probability scores, and inserts tags into the translated document at corresponding locations, ensuring accurate formatting and tagging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual translation is used to translate documents with tags, then translation can be performed, but the formatting and tags are lost or incorrect due to human translators' lack of understanding
Solution Approach 1:
The system segments the translation process into two distinct phases: (1) human translators translate only the text content without tags, and (2) a computational algorithm separately restores tags by aligning the translated text with the original document structure. This segmentation allows translators to focus on linguistic accuracy while the algorithm handles structural integrity, resolving the contradiction between translation quality and tag preservation.
Solution Approach 2:
The patent introduces an intermediary alignment algorithm that acts as a bridge between the translated text and the original document tags. This intermediary component uses hidden Markov models and statistical alignment to map translated text segments back to their corresponding original segments, thereby recovering tag positions and structures without requiring translators to understand markup languages.
2Productivity
If human translators translate documents with tags, then translation can be completed, but the process is time-consuming and error-prone
Solution Approach 1:
By dividing the translation workflow into text translation (human) and tag restoration (algorithmic), the system achieves both high productivity and high reliability. Translators work only with plain text at normal speed, while the automated alignment process rapidly and accurately restores formatting, combining the advantages of human linguistic capability with machine precision and speed.
Solution Approach 2:
The system enables the translated document to self-correct its own formatting through automated alignment. The algorithm compares the translated text structure with the original document structure and automatically reinstates tags in their appropriate positions, eliminating the need for translators to manually handle formatting while ensuring consistency.
3Manufacturing precision
If translators are trained to understand tags, then formatting can be preserved, but the complexity and training time increase
Solution Approach 1:
Instead of making translators understand complex markup tags, the system introduces an intermediary alignment algorithm that handles tag restoration. This intermediary component uses statistical models to automatically map translated text to original text structures, achieving accurate tag placement without increasing translator complexity or training requirements.
Solution Approach 2:
The patent replaces the mechanical approach of training human translators to understand and manually apply tags with an automated computational system. The hidden Markov model and statistical alignment algorithms substitute for human cognitive processing of markup languages, achieving the same tag placement accuracy through mathematical modeling rather than human expertise.
Data Source
AI summary
A computer-implemented method includes receiving a document and a request to translate the document to a different language, the document including at least one tag associated with a first portion of text within the document, receiving a manual translation of the document translated by a human translator but not including the at least one tag, generating a plurality of alignments between the document and the manual translation using a statistical alignment model, selecting one of the plurality of alignments based on a likelihood that the first portion of text in the document corresponds to an aligned second portion of text within the manual translation, mapping a location of the tag in the document to a corresponding location within the manual translation based on the selected alignment, and inserting the at least one tag into the manual translation at the corresponding location to obtain a modified manual translation of the document.