Dynamic Beam Width Adjustment for Neural Machine Translation
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Solution Overview
Problem
Existing beam search algorithms for language translation use a static beam width, which can lead to sub-optimal translations due to fixed selection of alternatives at each time step, resulting in either inefficient computations or loss of accurate predictions.
Innovation Solution
Implementing a dynamic beam search algorithm that adjusts the beam width based on probability differences using a fully connected neural network (FCNN) at each time step, allowing for a variable number of best alternative words to be selected dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a static beam width is used in beam search algorithms, then the algorithm structure is simple and easy to implement, but translation quality deteriorates due to fixed selection of alternatives at each time step
Solution Approach 1:
The patent applies the dynamics principle by transforming the static beam width into a dynamic parameter that changes at each time step. The beam width is no longer fixed but is instead determined by the probability distribution of vocabulary words at each translation step, allowing the search algorithm to adapt to different contextual requirements and improve translation quality while maintaining reasonable computational complexity
Solution Approach 2:
The patent implements parameter changes by modifying the beam width parameter based on the probability distribution characteristics of the vocabulary words. The beam width is dynamically adjusted according to the entropy and probability spread of candidate words at each time step, enabling the algorithm to optimize the balance between exploration and exploitation for better translation results
2Manufacturing precision
If a larger beam width is used to select more alternatives, then translation accuracy improves by considering more possibilities, but computational efficiency deteriorates due to increased processing load
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the beam width parameter based on the probability distribution of vocabulary words. When probability distributions are concentrated (low entropy), a smaller beam width is used to maintain efficiency. When distributions are diffuse (high entropy), a larger beam width is selected to capture more possibilities, thus optimizing the trade-off between accuracy and computational cost at each time step
Solution Approach 2:
The patent implements partial action by selecting only the necessary number of alternatives at each time step based on the probability distribution characteristics. Instead of always using a fixed large beam width, the algorithm uses a smaller beam width when sufficient accuracy can be achieved with fewer alternatives, avoiding unnecessary computational resources while maintaining translation quality
3Productivity
If a smaller beam width is used to reduce computations, then computational efficiency improves, but translation accuracy deteriorates due to loss of accurate predictions
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the beam width based on the probability distribution entropy and characteristics at each time step. When the probability distribution shows high concentration (low entropy), a smaller beam width is used to maximize efficiency. When the distribution is more uniform or has long tails (high entropy), the beam width is increased to prevent loss of accurate predictions, thus optimizing the balance between computational efficiency and translation accuracy
4Device complexity
If the beam width is fixed throughout the translation process, then the algorithm is simple to implement, but it cannot adapt to different contexts of the sentence
Solution Approach 1:
The patent applies the dynamics principle by making the beam width a dynamic parameter that changes at each time step based on the probability distribution of vocabulary words. This allows the algorithm to adapt to different contextual requirements during translation, such as adjusting the search depth when encountering ambiguous words versus common words, thereby improving context adaptation without significantly increasing implementation complexity
Solution Approach 2:
The patent implements feedback by using the probability distribution information from the vocabulary words to dynamically adjust the beam width at each time step. The probability distribution serves as feedback about the contextual requirements, and the beam width is adjusted accordingly, creating a closed-loop system that adapts to different sentence contexts while maintaining reasonable algorithmic complexity
Data Source
AI summary
A method, computer program product, and computer system for translating, using a beam search, a source sentence in a source language into a target sentence in a target language by an iterative process. Each iteration of the iterative process includes: generating, using a sequence-to-sequence model, probability vectors of conditional probabilities of respective vocabulary words in the target language being translations of a source word in the source sentence; sorting the probabilities in the probability vectors; generating probability difference vectors containing numerical differences between adjacent elements in respective sorted probability; vectors determining, by a fully connected neural network (FCNN), a best beam width B using the probability difference vectors as input to the FCNN; selecting B vocabulary words and B target vectors corresponding to the B highest conditional probabilities; and after all words in the source sentence have been translated, outputting the B target vectors generated in the last iteration.