Neural Machine Translation with Bidirectional LSTM and Attention
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Solution Overview
Problem
Current neural machine translation systems face challenges in efficiently translating text between languages due to limitations in capturing subtle irregularities in language structures and gradient flow during training, which affects translation quality and inference time.
Innovation Solution
A neural machine translation system utilizing an encoder-decoder architecture with bi-directional and uni-directional LSTM layers, residual connections, and an attention subsystem, which tokenizes input sequences into wordpieces and performs quantized computations to improve translation accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard neural network architectures are used for machine translation, then the system can process text translation tasks, but it fails to capture subtle irregularities in language structures effectively
Solution Approach 1:
The patent segments the neural network into specialized components: bidirectional LSTM for capturing contextual irregularities, unidirectional LSTM for sequential processing, attention mechanisms for focusing on relevant source words, and residual connections for preserving gradient flow. This segmentation allows each component to specialize in capturing different aspects of language structure irregularities.
Solution Approach 2:
The patent creates a composite neural network architecture that combines multiple LSTM types, attention mechanisms, and residual connections into a unified translation system. This composite structure leverages the strengths of each component to effectively capture subtle language irregularities that single architectures cannot handle.
2Reliability
If complex neural network architectures with multiple layers are used to improve translation quality, then translation accuracy improves, but training and inference time increases
Solution Approach 1:
The bidirectional LSTM performs preliminary processing of the entire source sequence to capture contextual irregularities before the main translation generation. The attention mechanism pre-identifies relevant source words for each target position, reducing the computational burden during sequential generation and improving overall efficiency.
Solution Approach 2:
The residual connections enable continuous gradient flow through the deep network during training, allowing the complex multi-layer architecture to be trained effectively without vanishing gradient problems. This continuity maintains training efficiency despite the increased network depth required for high translation quality.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for neural machine translation. One of the systems includes an encoder neural network comprising: an input forward long short-term memory (LSTM) layer configured to process each input token in the input sequence in a forward order to generate a respective forward representation of each input token, an input backward LSTM layer configured to process each input token in a backward order to generate a respective backward representation of each input token and a plurality of hidden LSTM layers configured to process a respective combined representation of each of the input tokens in the forward order to generate a respective encoded representation of each of the input tokens; and a decoder subsystem configured to receive the respective encoded representations and to process the encoded representations to generate an output sequence.