Machine Translation for Spoken Dialog System Localization
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Solution Overview
Problem
Localizing spoken dialog systems across different languages is a costly and time-consuming process due to the need for extensive data collection, transcription, and annotation, especially when there is a lack of experts and high volumes of data to process, making it impractical for languages with low call volumes.
Innovation Solution
A system and method that utilize machine translation to translate utterances from a source language into a target language, using existing transcriptions and annotations from a source language spoken dialog system to train semantic classifiers, thereby eliminating the need for new data collection and expert labor, and allowing for scalable localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If machine translation is used to translate utterances from source language to target language, then localization cost and time are reduced, but translation accuracy may be compromised
Solution Approach 1:
The patent uses machine translation as an intermediary step to bridge source language and target language, accepting that automated translation may not be perfectly accurate but is sufficient for training purposes. The system mediates between the need for quick localization and the requirement for training data by using machine-translated text as a intermediate representation that can be further processed or corrected.
Solution Approach 2:
The patent creates copies of source language training data and translates them to target language using machine translation. Instead of collecting original target language data, the system copies existing source language utterances, transcriptions, and annotations, then translates the textual components while preserving the structural relationships and semantic annotations.
2Reliability
If extensive data collection and annotation is performed for target language, then classifier accuracy is improved, but localization cost and complexity increase
Solution Approach 1:
The patent makes the training pipeline universal by using the same data collection, transcription, and annotation processes for both source and target languages. The system treats both languages equally through the same workflow, with machine translation serving as the differentiating step. This universal approach reduces complexity by eliminating the need for separate, language-specific annotation protocols.
Solution Approach 2:
The system enables self-service localization by automatically generating target language training data from source language data without requiring human experts in the target language. The machine translation component serves itself by translating utterances and maintaining annotations automatically, eliminating the need for manual target language annotation services.
3Adaptability or versatility
If machine translation is used for localization, then scalability to multiple languages is improved, but dependency on translation quality increases
Solution Approach 1:
The patent performs machine translation as a preliminary action before training the classifier. By translating source language utterances to target language beforehand and using these translated texts for training, the system prepares the training data in advance. This preliminary translation allows the classifier to learn from target language text without requiring real-time translation during operation.
Solution Approach 2:
The system changes the language parameter of the training data from source language to target language through machine translation, while keeping all other training parameters (annotations, transcriptions, data structures) unchanged. This parameter transformation enables the same training pipeline to work across multiple languages by simply changing the language of the input text.
Data Source
AI summary
A system and method for localizing a spoken dialog system is disclosed. Source data from a source language spoken dialog system is accessed, including semantic annotations and transcriptions of a plurality of utterances. The transcriptions are machine-translated into a target language. Semantic classifiers are trained on the machine translated transcriptions and the source language semantic annotations.