Language Model Translation Context Vector Attention Mechanism
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Solution Overview
Problem
Current language model translation methods face challenges in accurately translating speech recognition outputs due to the lack of effective processing and representation of context vectors, leading to suboptimal translation quality.
Innovation Solution
A processor-implemented language model training method that assigns context vectors to target translation vectors, generates feature vectors through character embedding and positional encoding, and uses an attention mechanism to create a representative vector, which is then used to train a recurrent neural network (RNN) for improved translation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional language model translation methods are used, then the translation process is simpler, but translation accuracy deteriorates due to ineffective processing of context vectors
Solution Approach 1:
The patent segments the translation model into distinct functional components: context vector processing module, character embedding layer, positional encoding layer, and attention mechanism module. Each component handles a specific aspect of contextual information processing, allowing the complex task of accurate translation to be divided into manageable processing stages that collectively improve translation accuracy without overwhelming complexity
Solution Approach 2:
The patent introduces context vectors as intermediary representations that mediate between the input text and translation output. These context vectors serve as a bridge that carries contextual information through the model, enabling more accurate translation decisions. The attention mechanism further uses these context vectors as intermediaries to selectively focus on relevant parts of the input when generating translations
2Reliability
If context vectors are not effectively processed, then the model is simpler, but translation quality deteriorates
Solution Approach 1:
The patent applies preliminary action by processing context vectors through character embedding and positional encoding before the main translation task. This pre-processing of contextual information ensures that when the attention mechanism operates, it already has well-structured contextual data to work with, improving translation quality while distributing computational complexity across multiple preparatory stages
Solution Approach 2:
The patent transforms context vectors from simple sequential data into multi-dimensional representations through character embedding (mapping characters to dense vector spaces) and positional encoding (adding positional information as an additional dimension). This dimensional transformation enriches the contextual information available to the model, significantly improving translation quality by capturing nuanced relationships that would be lost in simpler representations
3Measurement precision
If character correlations are not captured, then processing is faster, but translation precision deteriorates
Solution Approach 1:
The patent replaces traditional mechanical sequential processing with parallel attention-based processing. Instead of processing characters one by one in strict sequence, the attention mechanism simultaneously evaluates all character-position combinations in the context vector, capturing character correlations through parallel computations. This substitution maintains high character-level precision while reducing processing time through efficient parallel operations
Data Source
AI summary
A language model training method and an apparatus using the language model training method are disclosed. The language model training method includes assigning a context vector to a target translation vector, obtaining feature vectors based on the target translation vector and the context vector, generating a representative vector representing the target translation vector using an attention mechanism for the feature vectors, and training a language model based on the target translation vector, the context vector, and the representative vector.