Dynamic Vocabulary Probability Adjustment for Interactive Dialogue
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Solution Overview
Problem
Interactive translation and dialogue systems face inaccuracies and errors, particularly when encountering unknown words or failing to recognize words previously output by the system, leading to misinterpretations in conversations across languages.
Innovation Solution
The method enhances these systems by leveraging lexical entrainment, adjusting the probabilities of words in the language model based on their recent usage, ensuring that recently output words are given higher probabilities in subsequent conversations, thus improving recognition and translation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system uses a fixed vocabulary with static word probabilities, then the system structure remains simple, but the system fails to recognize words previously output by the system and cannot adapt to user terminology
Solution Approach 1:
The patent applies dynamics by transforming the static vocabulary probability distribution into a dynamic one that adapts during conversation. The system continuously updates word probabilities based on recently output words, allowing the vocabulary to evolve dynamically rather than remaining fixed. This enables the system to adapt to user terminology and recognize previously output words without requiring a complete restructuring of the vocabulary system.
Solution Approach 2:
The patent implements feedback by using the system's own output as input for subsequent processing. Recently output words are fed back into the probability adjustment mechanism, creating a closed-loop system that learns from its own conversations. This feedback loop allows the system to maintain consistency in terminology and adapt to user preferences through iterative probability adjustments based on conversation history.
2Measurement precision
If the system adjusts word probabilities dynamically based on recent usage, then the recognition accuracy of previously output words improves, but the processing complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-defining the probability adjustment mechanism and the set of recently output words to be monitored. Rather than performing complex analysis during real-time processing, the system prepares the probability adjustment framework in advance, specifying how probabilities will be modified based on predetermined criteria. This reduces real-time processing complexity while maintaining high recognition accuracy.
Solution Approach 2:
The patent implements parameter changes by modifying the probability parameters of the vocabulary distribution based on recent word usage. Instead of changing the entire vocabulary structure or adding complex processing layers, the system adjusts the probability parameters dynamically. This allows for improved recognition accuracy through simple parameter modulation rather than complex structural changes.
3Adaptability or versatility
If the system maintains a large static vocabulary, then the system can handle diverse terminology, but the system cannot adapt to user-specific word preferences and may misrecognize familiar words
Solution Approach 1:
The patent resolves this contradiction by making the vocabulary probability distribution dynamic rather than static. The system maintains a comprehensive vocabulary to handle diverse terminology but adjusts the active probability weights based on recent usage patterns. This dynamic adjustment allows the system to reliably recognize user-preferred terms while maintaining the capability to handle diverse terminology through the underlying comprehensive vocabulary.
Solution Approach 2:
The patent applies local quality by differentiating the treatment of different words in the vocabulary based on their recent usage context. Rather than uniformly treating all vocabulary words, the system applies localized probability adjustments to specific words that have been recently output. This allows high-reliability recognition of user-specific terms while maintaining the broader vocabulary's ability to handle diverse terminology.
Data Source
AI summary
The present invention relates to a method and apparatus for enhancing interactive translation and dialogue systems. In one embodiment, a method for conducting an interactive dialogue includes receiving an input signal in a first language, where the input signal includes one or more words, processing the words in accordance with a vocabulary, and adjusting a probability relating to at least one of the words in the vocabulary for an output signal. Subsequently, the method may output a translation of the input signal in a second language, in accordance with the vocabulary. In one embodiment, adjusting the probability involves adjusting a probability that the word will be used in actual conversation.