Polysemous Word Disambiguation in Neural Machine Translation
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Solution Overview
Problem
Existing machine translation technologies face challenges in accurately translating polysemous words, which have multiple semantic meanings, due to the inability to recognize the correct semantic meaning in context.
Innovation Solution
A translation method that uses a trained translation model to determine the target semantic meaning of a polysemous word by processing word vector sequences and employing a disambiguation rule, ensuring accurate translation by leveraging context information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a traditional translation model is used to process polysemous words, then the translation process is simple and fast, but the translation accuracy deteriorates because the model cannot recognize the correct semantic meaning of polysemous words in context
Solution Approach 1:
The patent segments the translation model into multiple specialized components: a polysemous word identification module that detects ambiguous words, a semantic meaning determination module that resolves ambiguity using context, and a translation module that performs the actual translation. This segmentation allows each component to specialize in specific tasks, improving overall accuracy while managing complexity through modular design
Solution Approach 2:
The patent introduces an intermediary semantic analysis layer between the input text and the translation process. This intermediary module processes polysemous words by analyzing contextual clues and determining the correct semantic meaning before passing information to the translation module, thereby improving translation accuracy without requiring complete model redesign
2Measurement precision
If the translation model is trained with parallel corpora from different languages, then the translation capability is improved, but the ability to accurately disambiguate polysemous words deteriorates because the training data does not focus on semantic context within the same language
Solution Approach 1:
The patent applies local quality by creating specialized training data that focuses specifically on polysemous word contexts within the source language. Instead of relying solely on general parallel corpora, the system enhances training with sentences that contain polysemous words annotated with their correct semantic meanings, allowing the model to learn contextual disambiguation patterns specific to ambiguous vocabulary
Solution Approach 2:
The patent performs preliminary semantic analysis and disambiguation before the main translation process. By pre-processing the input text to identify and resolve polysemous words using context-aware algorithms, the system prepares clarified semantic representations that guide the subsequent translation step, improving accuracy without requiring extensive retraining on specialized data
Data Source
AI summary
The translation method includes: obtaining a to-be-translated sentence including a polysemous word expressed in a first language; determining a target semantic meaning of the polysemous word according to a disambiguation rule, where the disambiguation rule indicates to determine the target semantic meaning of the polysemous word by using a translation model, the translation model may perform data processing on a word vector sequence corresponding to the to-be-translated sentence to obtain the semantic meaning of the polysemous word in the to-be-translated sentence, the translation model is obtained by training a parallel training sentence pair, and both training sentences in the parallel training sentence pair are the first language; and determining a translation result of the to-be-translated text based on the target semantic meaning, where the translation result includes content whose target semantic meaning is expressed in a second language, and the first language is different from the second language.