Conditional Source Target Memory for Neural Machine Translation
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Solution Overview
Problem
Current neural machine translation (NMT) systems face challenges in translating rare words and idioms, and perform poorly across multiple topic domains due to limitations in handling local phrase level similarities and noisy contexts.
Innovation Solution
The implementation of a conditional source target memory (CSTM) system that uses a gating operation to combine source encoding and contextual information from similar n-grams in both languages, enabling improved translation by leveraging local phrase level similarities and noisy contexts through a semiparametric approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current NMT systems use standard neural network architectures and training techniques, then the systems can process general translation tasks, but they struggle to translate rare words, idioms and phrases and perform poorly across multiple topic domains
Solution Approach 1:
The system retrieves similar source n-grams and their target translations from a corpus before performing the main translation task. This preliminary retrieval of relevant contextual information allows the model to handle rare words and domain-specific terminology more effectively by leveraging pre-fetched similar examples.
Solution Approach 2:
The patent introduces an intermediary retrieval mechanism that fetches similar n-grams from a corpus and uses a gating operation as a mediator to selectively combine retrieved contextual information with the main translation model. This intermediary layer enables the model to adapt to rare words and multiple domains without retraining.
2Adaptability or versatility
If the NMT system retrieves and processes similar n-grams from a corpus, then the system can improve translation of rare words and adapt to new domains, but the system complexity increases due to additional retrieval and gating operations
Solution Approach 1:
The translation system is segmented into distinct functional modules: an n-gram retrieval component that fetches similar examples from a corpus, a gating operation module that selectively combines retrieved information with main model outputs, and the core translation model. This segmentation allows each component to be optimized independently while maintaining overall system adaptability.
3Measurement precision
If the system uses n-gram level retrieval with local phrase level similarities, then the system can retrieve useful neighbors for translation even when overall sentence similarity is low, but the system requires more computational resources to process and compare n-grams
Solution Approach 1:
The system focuses on local n-gram level similarities rather than requiring global sentence similarity. By breaking down sentences into n-grams and retrieving similar local phrases from the corpus, the system can find relevant translation examples even when the overall sentence structure or topic differs significantly, improving precision for rare words and idioms.
Data Source
AI summary
Adapters for neural machine translation systems. A method includes determining a set of similar n-grams that are similar to a source n-gram, and each similar n-gram and the source n-gram is in a first language; determining, for each n-gram in the set of similar n-grams, a target n-gram is a translation of the similar n-gram in the first language to the target n-gram in the second language; generating a source encoding of the source n-gram, and, for each target n-gram determined from the set of similar n-grams determined for the source n-gram, a target encoding of the target n-gram and a conditional source target memory that is an encoding of each of the target encodings; providing, as input to a first prediction model, the source encoding and the condition source target memory; and generating a predicted translation of the source n-gram from the first language to the second language.