Interactive Machine Translation Vocabulary Adjustment
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Solution Overview
Problem
Machine translation using sequence-to-sequence models often results in inaccurate translations that require frequent manual corrections, leading to inefficiencies and reduced translation quality.
Innovation Solution
An interactive machine translation method and apparatus that allows users to adjust specific vocabularies in translation results, with the system providing candidate replacements and adaptively adjusting surrounding vocabulary sequences based on user input to generate improved translations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine translation uses sequence-to-sequence model to translate source statement into target statement, then translation speed is improved, but translation accuracy deteriorates requiring frequent manual corrections
Solution Approach 1:
The system implements feedback by detecting user corrections to translation results and using these corrections to generate candidate vocabularies for replacement. The translation model continuously learns from user feedback, adjusting its output to improve accuracy while maintaining efficient automated translation processing.
Solution Approach 2:
The system enables self-service by automatically generating candidate vocabulary replacements when users correct translation errors. The model autonomously identifies potential improvement areas and provides correction options without requiring extensive manual intervention, allowing users to quickly refine translations by selecting from suggested candidates.
2Manufacturing precision
If user manually adjusts multiple errors in translation results, then translation accuracy is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary action by pre-generating multiple candidate vocabulary replacements before the user needs to make corrections. When a user identifies an error, the system has already prepared several alternative vocabularies based on the context and translation model predictions, allowing the user to quickly select the best option without spending time generating alternatives manually.
Solution Approach 2:
The system acts as an intermediary by providing a set of candidate vocabulary options between the automated translation model and the user's final decision. This intermediary layer of suggested corrections reduces the cognitive load and time required for users to identify appropriate replacements, bridging the gap between machine-generated translations and human judgment.
3Manufacturing precision
If system provides multiple candidate vocabularies for replacement, then translation quality is improved, but system complexity increases
Solution Approach 1:
The system applies parameter changes by dynamically adjusting the number and type of candidate vocabularies presented to users based on the specific translation context, error type, and confidence levels of the translation model. This allows the system to provide high-quality corrections when needed while maintaining simpler operation for straightforward translations, effectively managing complexity through adaptive parameter adjustment.
Data Source
AI summary
Provided are an interactive machine translation method and apparatus, a device, and a medium. The method includes: acquiring a source statement input by a user; translating the source statement into a first target statement; determining whether the user adjusts a first vocabulary in the first target statement; and in response to determining that the user adjusts the first vocabulary in the first target statement, acquiring a second vocabulary for replacing the first vocabulary, and adjusting, based on the second vocabulary, a vocabulary sequence located in a front of the first vocabulary and a vocabulary sequence located behind the first vocabulary in the first target statement to generate a second target statement.