Text Translation Encoder-Refiner-Decoder Framework
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Solution Overview
Problem
The encoder-decoder framework used in text translation often results in poor translation quality, especially for long sentences, due to deviations in translation.
Innovation Solution
A text translation method that introduces a context vector during encoding to enhance the representation of the to-be-translated text sequence, improving understanding and quality by using an encoder-refiner-decoder framework, which includes a refiner to refine the source language representation and dynamically determine if re-encoding is necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the encoder-decoder framework is used for text translation, then the translation process can be implemented, but the translation quality deteriorates, especially for long sentences
Solution Approach 1:
The patent segments the translation process into three distinct modules: encoder, refiner, and decoder. The refiner module specifically processes the context vector to refine the source language representation before it reaches the decoder. This segmentation allows each module to specialize in specific tasks, improving overall translation quality while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent introduces a context vector as an intermediary element that carries semantic information from the encoder to the refiner and then to the decoder. This context vector acts as a mediator that enhances the representation of source language text, particularly for long sentences, by capturing and transmitting essential semantic context throughout the translation process.
2Measurement precision
If the encoder-decoder framework is used, then translation can be performed, but deviations occur in translation especially for long sentences
Solution Approach 1:
The refiner module performs preliminary refinement of the context vector before the decoding process begins. By pre-processing and refining the semantic representation in advance, the system prepares more accurate source language representations that reduce deviations during the actual translation decoding, particularly benefiting long sentences where context maintenance is critical.
Solution Approach 2:
The patent implements a feedback mechanism where the refiner module receives the context vector from the encoder, processes it to generate refined representations, and feeds these refined representations back to the decoder. This feedback loop ensures that semantic information is continuously refined and maintained throughout the translation process, reducing accuracy loss especially for long source sentences.
Data Source
AI summary
A text translation method includes: obtaining a to-be-translated text sequence; encoding the to-be-translated text sequence, to obtain a first hidden state sequence; obtaining a first state vector; generating a second hidden state sequence according to the first state vector and the first hidden state sequence; generating a context vector corresponding to a current word according to the second hidden state sequence and the first state vector; determining a second target word according to the context vector, the first state vector, and a first target word. The first state vector corresponds to a predecessor word of a current word, the current word is a to-be-translated word in the source language text, the predecessor word is a word that has been translated in the source language text. The first target word is a translation result of a predecessor word, and the second target word is a translation result of the current word.