Build custom language knowledge bases
Clean and align a professional parallel corpus
Collect and generate targeted data
Develop industry-specific language models
Fine-tune parameters using algorithms
Enable intelligent translation and content generation
Address industry-specific language challenges
Enhance internal and external workflows
Tailor solutions to business scenarios
Build a four-dimensional evaluation system
Optimize models for targeted business scenarios
Validate performance using test sets
Establish MLOps for algorithm learning
In today’s era of globalization and regional economic integration, cross-language communication has evolved from a simple "auxiliary tool" to a "strategic necessity" for companies with global ambitions. Multinational enterprises face growing challenges, particularly in high-frequency scenarios like business negotiations, technical collaboration, and market research, where generic translation models often fall short. Issues like "inaccurate translations" and "mistranslation of technical terms" in less commonly used (minority) languages can derail critical operations. Addressing these specific issues is key to achieving successful global management.
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