Project Background
AIxBlock, a leading AI training data provider, needed to expand its banking-domain natural language understanding (NLU) dataset. The goal was to collect realistic email and instant messaging-style communications simulating professional interactions in tier-one banking environments across four Asian markets: Japan, Mainland China, Hong Kong, and Singapore.
The Challenge
This project presented several unique difficulties:
- Tight Timeline: 15 calendar days from start to delivery for 5,040 sentences
- Complex Linguistic Requirements: Four distinct locales with specific language conventions—Japan (casual Japanese with hiragana-heavy slang), Hong Kong (vernacular Cantonese with code-switching), Mainland China (Simplified Chinese with Mandarin-English mixing), and Singapore (localized English with Singlish influences)
- Strict Quality Standards: All content had to be 100% human-generated, with exactly one sentence per entry (15-25 words), correct embedding of 84 lexicon terms per sub-behavior, and a 50/50 formal/informal tone balance (±10%)
- Domain Expertise Required: Every contributor had to be a banking-domain SME with native fluency
Our Solution
Smart Language Service deployed a three-pronged approach to ensure success:
1. Rapid Contributor Recruitment & Vetting
Within 72 hours of project kickoff, we recruited and vetted 40+ banking-domain professionals across all four geographies—each with native fluency and at least 3 years of banking industry experience. Gender balance was maintained within ±10% as required.
2. Structured Content Generation Framework
We developed a guided workflow that ensured every contributor understood the linguistic nuances required:
- For Japan: Emphasized short sentences, hiragana-heavy writing, and appropriate use of emojis/kaomoji to convey emotional tone
- For Hong Kong: Focused on authentic written Cantonese characters and natural English code-switching commonly seen in WhatsApp and social media banking communications
- For Mainland China: Incorporated internet slang, digital expressions, and regional Mandarin-dialect influences while maintaining natural conversational flow
- For Singapore: Captured the unique Singlish flavor with localized banking terminology
3. Multi-Layer Quality Control
We implemented a rigorous QC process:
- Automated validation: Sentence length check (15-25 words), lexicon embedding verification (exact match + 2 semantic variants per lexicon)
- Manual review: Native linguists verified regional authenticity, tone balance (formal/informal), and domain accuracy
- Final sampling: Random 20% sample reviewed by senior project managers before delivery
Results & Delivery
The project was delivered on time with zero quality rejections:
- 5,040 sentences delivered across 4 locales (1,260 per geography)
- 100% acceptance rate—all content passed AIxBlock's Review Period without conditional acceptance or rejection
- 0% LLM-generated content—every sentence was 100% human-generated and verified
- Perfect lexicon coverage—84 sub-behaviors × 5 lexicons × (1 exact + 2 semantic variants) fully covered
- Balanced tone distribution—Maintained 50/50 formal/informal ratio within ±10% across all locales
Conclusion
This project demonstrates Smart Language Service's ability to execute complex, multi-locale data collection projects under tight deadlines without compromising quality. By combining a robust contributor network, structured workflows, and rigorous QA processes, we delivered a dataset that met the highest standards of linguistic authenticity and domain accuracy.