Statistical Translation Memory Using Probability Tuples
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Solution Overview
Problem
Current machine translation systems face challenges in accurately translating natural language sentences due to the complexity of probability calculations and the need for efficient extraction of relevant translation pairs, particularly in systems like statistical MT that rely on language models and translation models.
Innovation Solution
The development of a statistical translation memory (TMEM) system that trains a translation model using naturally generated translation pairs, extracts tuples with probability information, and selects the most probable or frequently occurring target language phrases as translation equivalents, incorporating word alignment and stochastic processes to improve translation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a statistical translation model is trained with naturally generated translation pairs, then translation accuracy is improved, but the computational complexity and difficulty of extracting relevant translation pairs increases
Solution Approach 1:
The patent segments the complex task of translation pair extraction into manageable components by organizing translation pairs into structured tuples containing source phrase, target phrase, and probability information. This segmentation allows the system to process and store translation data in an organized manner, reducing the overall computational complexity while maintaining translation accuracy.
Solution Approach 2:
The patent performs preliminary actions by pre-processing naturally generated translation pairs into structured tuples with embedded probability information before actual translation tasks. This pre-processing step includes extracting phrases, calculating probabilities, and organizing data into the translation memory structure, which simplifies subsequent translation operations and reduces real-time computational complexity.
2Adaptability or versatility
If multiple target language phrases are paired with the same source language phrase, then translation flexibility is improved, but the difficulty of selecting the correct translation equivalent increases
Solution Approach 1:
The patent implements feedback mechanisms by associating probability information with each translation pair in the memory. When selecting translation equivalents, the system uses this probability feedback to evaluate and rank multiple candidate phrases, making the selection process more systematic and accurate. The probability values serve as feedback signals that guide the decoder in choosing the most appropriate translation equivalent.
Solution Approach 2:
The patent changes the parameter of translation pair representation by incorporating probability information alongside source and target phrases. This parameter change transforms the selection process from a purely linguistic matching task to a probabilistic optimization task, where the decoder can systematically evaluate multiple candidates based on their associated probability values and select the optimal translation equivalent.
3Measurement precision
If probability information is extracted and stored for each translation pair, then translation quality is improved, but the memory requirements and data storage complexity increases
Solution Approach 1:
The patent extracts only the essential probability information from naturally generated translation pairs and stores it in a compact tuple structure within the translation memory. This selective extraction approach captures the necessary probabilistic data for improving translation quality while avoiding the storage of redundant or excessive information, thereby balancing translation quality improvement with manageable data storage requirements.
Data Source
AI summary
A statistical translation memory (TMEM) may be generated by training a translation model with a naturally generated TMEM. A number of tuples may be extracted from each translation pair in the TMEM. The tuples may include a phrase in a source language and a corresponding phrase in a target language. The tuples may also include probability information relating to the phrases generated by the translation model.