Dialog Translation System Using Named Entity and Semantic Template Matching
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Solution Overview
Problem
Current machine translation methods exhibit poor translation effects and low accuracy in dialog-oriented scenarios due to colloquialisms and omitted syntactic elements, leading to ambiguity and incorrect translations.
Innovation Solution
A translation method that determines named entities and semantic templates specific to the dialog task, using a correspondence between languages to ensure accurate translation by identifying and translating named entities and semantic templates, and allowing for modification to improve clarity and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If common machine translation methods are used for dialog field, then translation speed is maintained, but translation accuracy deteriorates due to colloquialism and omitted syntactic elements
Solution Approach 1:
The translation system is segmented into multiple specialized modules: dialog context analysis module, named entity recognition module, semantic role labeling module, and translation generation module. Each module handles a specific aspect of dialog translation, allowing the system to process colloquial expressions and omitted elements systematically without requiring complete syntactic structure
Solution Approach 2:
The system performs preliminary actions by building dialog context models and named entity dictionaries before translation. The context analysis module pre-processes dialog history to identify referenced entities and establish semantic relationships, preparing structured information that guides subsequent translation steps to accurately handle colloquial references
2Measurement precision
If analysis-based machine translation is applied to dialog field, then translation quality improves through semantic understanding, but processing time increases due to comprehensive analysis requirements
Solution Approach 1:
The system applies different analysis depths to different parts of the dialog. Named entities and key semantic roles receive detailed analysis while common words use simpler processing. The semantic role labeling focuses specifically on argument structures relevant to the dialog task, avoiding unnecessary comprehensive syntactic analysis of every word
Solution Approach 2:
The system performs partial semantic analysis by identifying only the critical semantic roles and named entities needed for accurate translation, rather than complete linguistic analysis. This selective approach captures sufficient meaning for dialog translation while reducing processing overhead
3Measurement precision
If traditional translation software is used for dialog translation, then general translation capability is maintained, but dialog-specific translation accuracy deteriorates due to lack of context awareness
Solution Approach 1:
The translation system dynamically adapts to different dialog contexts by adjusting its analysis depth and selecting appropriate translation strategies based on the identified dialog task type. The context model is continuously updated during dialog progression, allowing the system to maintain high accuracy across varying dialog scenarios while preserving general translation capability
Data Source
AI summary
Embodiments of the present invention provide a translation method and apparatus, and relate to the field of machine translation. The method includes: obtaining a to-be-translated sentence, where the to-be-translated sentence is a sentence expressed in a first language; determining a first named entity set in the to-be-translated sentence, and an entity type of each first named entity in the first named entity set; determining, based on the first named entity set and the entity type of each first named entity, a second named entity set expressed in a second language; determining a source semantic template of the to-be-translated sentence, and obtaining a target semantic template corresponding to the source semantic template from a semantic template correspondence; and determining a target translation sentence based on the second named entity set and the target semantic template.