Context-Aware Review Translation Using Item Page Keywords
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Solution Overview
Problem
Conventional machine translation technologies often produce inaccurate translations of user-generated reviews due to the lack of sufficient context, particularly when the review text is short or lacks additional information, leading to incorrect interpretations of words with multiple meanings.
Innovation Solution
A translation system that utilizes context from an item page outside the review text, including keywords derived from item page text, images, and videos, to enhance machine translation accuracy by employing a conditional deep neural encoder-decoder model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine translation technology is used to translate review text, then translation speed is maintained, but translation accuracy deteriorates due to lack of context
Solution Approach 1:
The system performs preliminary extraction of context information from the item page before translation. Keywords, images, and product information are gathered in advance and provided to the translation model as contextual input, enabling more accurate translation of review text that refers to specific product attributes or features.
Solution Approach 2:
The patent introduces an intermediary context extraction and processing layer between the source review text and the translation model. This intermediary component extracts keywords, images, and product information from the item page, processes them into relevant contextual representations, and feeds them to the translation model to guide accurate translation.
2Measurement precision
If context from item page is extracted and used for translation, then translation accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the context extraction process into distinct modular components: keyword extraction module, image processing module, and product information retrieval module. Each module handles a specific type of context information independently, making the overall system more manageable and maintainable while improving translation accuracy through comprehensive context gathering.
Solution Approach 2:
The translation system is designed as a multi-functional platform that can handle multiple types of input (text, images, keywords) and adapt to different product types and review languages. The unified architecture processes various context information types through a common translation model, reducing overall system complexity despite the enhanced capabilities.
Data Source
AI summary
A translation system provides machine translations of review texts on item pages using context from the item pages outside of the review text being translated. Given review text from an item page, context for machine translating the review text is determined from the item page. In some aspects, one or more keywords are determined based on text, images, and/or videos on the item page. The one or more keywords are used as context by the machine translator to translate the review text from a first language to a second language to provide translated review text, which can be presented on the item page.