Adaptive Machine Translation via User Context
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Solution Overview
Problem
Existing machine translation systems lack the ability to dynamically adapt to the usage scenes of individual users, relying on pre-classified scenes that do not account for real-time user profiles and locations, resulting in suboptimal translation accuracy.
Innovation Solution
A machine translation apparatus that acquires user profile data and current location information to generate an adaptive model using reference data, dynamically learning and updating confidence scores for translated sentences to provide contextually relevant translations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-classified usage scenes are used for machine translation, then the system structure remains simple, but translation accuracy deteriorates because the system cannot adapt to real-time user profiles and locations
Solution Approach 1:
The patent implements dynamic adaptation by continuously updating the adaptive model with real-time user profile data and location information. The system transitions from static pre-classified scenes to dynamic scene generation based on current user context, allowing translation accuracy to adapt to changing user conditions without requiring complex manual reconfiguration
Solution Approach 2:
The system automatically generates adaptive models by acquiring user profile data and location information, then uses these to dynamically create usage scenes without user intervention. The machine translation apparatus self-adjusts to user needs by autonomously learning from reference data and updating its translation strategy based on accumulated user-specific patterns
2Measurement precision
If adaptive models are generated using user profile data and location information, then translation accuracy improves, but the amount of data processing and model generation increases
Solution Approach 1:
The system performs preliminary data acquisition by collecting user profile information and location data before translation tasks are executed. By preparing and storing this contextual information in advance, the system reduces real-time processing requirements and enables faster adaptive model generation when actual translation needs arise
Solution Approach 2:
The adaptive model dynamically adjusts translation parameters based on user-specific patterns learned from reference data. By changing model parameters to reflect individual user preferences and contextual factors, the system achieves higher accuracy without requiring complete reprocessing of all translation data, thus reducing processing time
Data Source
AI summary
According to one embodiment, a machine translation apparatus includes an input device which inputs a first language sentence; an additional information acquisition unit which acquires a first user or a current location of the first language sentence as a first additional information; a reference data storage device which stores second language reference data that are the relationships between second language sentences and at least one of a second user and a second user usage location of the second language sentences as a second additional information; a text data acquisition unit which acquire second language text data from the second language reference data including second additional information being the same at least one part of the first additional information; and a translation unit which translates the first language sentence to a second language sentence by using the second language text data.