Document Translation System Using User Feedback Corpus
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Solution Overview
Problem
Current machine translation technologies face inefficiencies due to the lack of readily available corpora, especially in scientific or technical domains, leading to inaccurate translations, and the high costs and time-consuming nature of human translations.
Innovation Solution
A document translation system that allows users to modify and store translations, using these modifications to improve subsequent translations by associating them with the original documents and employing them to enhance machine translation services, thereby creating a community-driven corpus for better translation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If corpus machine translation techniques are used to improve translation accuracy, then translation quality improves, but the difficulty of obtaining adequate corpora increases
Solution Approach 1:
The system enables users to automatically contribute to corpus creation by providing corrections and translations of documents they encounter. Users interact with the system normally and their inputs are automatically stored and utilized to train translation models, eliminating the need for manual corpus collection efforts.
Solution Approach 2:
The system implements feedback loops where user corrections and translations are fed back into the training process. Translation models continuously learn from user inputs, improving accuracy over time without requiring external corpus assembly efforts.
2Measurement precision
If human translators are employed to ensure accurate translations, then translation quality improves, but cost and time consumption increase
Solution Approach 1:
The system allows users to perform translation and correction tasks themselves through the interface, eliminating the need for professional translators. Users can correct translations and provide new translations directly in the system, which are then used to improve automated translation capabilities.
Solution Approach 2:
The system replaces the mechanical process of human translation with an automated machine translation system that continuously improves through user feedback. The machine translation component handles routine translation tasks while users focus on providing corrections and edge case inputs.
3Productivity
If machine translation is used to reduce cost and time, then productivity improves, but translation accuracy deteriorates
Solution Approach 1:
The system implements feedback mechanisms where users can correct translation errors and provide feedback on translation quality. These feedback inputs are automatically used to retrain translation models, continuously improving accuracy while maintaining high translation speed.
Solution Approach 2:
The system performs preliminary translation using machine translation to provide immediate results, then allows users to provide corrections and improvements. The system pre-processes translations quickly and uses user feedback to refine future translations, maintaining both speed and improving accuracy.
Data Source
AI summary
A document translation system is described. In various embodiments, the document translation system improves translations of text or speech (“documents”) that are performed by computing devices (“machine translations”). Upon translating a document into a target language, the system can provide a user interface containing both the original document and a translated document. The translated document can include portions that are translated automatically by a machine translation service, and other portions that have been amended or modified by users. Upon providing the translated document, the system may receive input from a user describing portions of the translated document that should be modified. These modifications may then be stored in corpora that the document translation system can employ during future requests to translate the document. When multiple modifications are stored, the system may select one of the stored modifications during translation, such as based on the document type, reputation of the user who provided the modification, and so forth. The stored corpora can then be employed to improve future machine translations.