Ctrip Machine Translation Platform: Architecture, Data Construction, Algorithm Design, and Performance Optimization

With the rapid internationalization of Ctrip, the demand for multilingual translation has grown beyond the capacity of human translators, prompting the development of a high‑quality, low‑cost machine translation service for tourism scenarios.

The platform, launched in 2019, now supports 34 languages (over 1,000 language pairs) and serves more than 150 entry points, handling 5 billion characters daily with sub‑100 ms latency and achieving translation quality 10 points above industry averages.

Leveraging Ctrip's private cloud and middle‑platform infrastructure, the service integrates front‑end interfaces, a preprocessing layer that extracts and normalizes content, a task‑space encoding module that injects scenario information into the model, and a Transformer‑based backend model.

Data construction relies on a closed‑loop pipeline that collects public corpora, purchases high‑quality private data, and harvests internal business data, generating up to 60 languages and 2.6 billion parallel sentences, along with a 5‑million‑term glossary and rich metadata tags.

Algorithmic innovations include:

Text preprocessing with tokenization, entity tagging, and normalization.

Task‑space information fusion using a learnable query vector to encode scenario tags, improving multilingual and multi‑scenario translation.

Term‑translation intervention via placeholder substitution and word‑alignment using cross‑attention matrices refined by a convolutional network.

Unsupervised quality estimation for low‑resource languages combining Muse coverage scores, word‑vector distance, and Transformer‑XL language‑model scores.

Performance optimizations encompass decoder caching, beam‑search early stopping, operator fusion, low‑precision FP16 inference, and GPU‑specific memory management, achieving up to 7× speedup on NVIDIA T4 with negligible BLEU loss.

Overall, continuous technical upgrades and platform operations have enabled Ctrip's machine translation service to meet diverse internal and external translation needs, while future work will focus on further personalization and scalability.

Figure 1: Ctrip Machine Translation Scenario Analysis

Figure 2: Platform Overview

Figure 3: Batch Processing Module

Figure 4: Basic Platform Architecture

Figure 6: Preprocessing Module

Figure 7: Task‑Space Fusion Model

Figure 8: Encoder/Decoder Task‑Space Inconsistency

Figure 9: Impact of Task‑Space on Model Performance

Figure 10: Word‑Alignment Mechanism

Figure 11: Placeholder Examples

Figure 13: Word‑Alignment Module Performance

Figure 14: Muse Unsupervised Cross‑Language Mapping

Figure 15: Transformer‑XL Language Model Training

Figure 16: Unsupervised Translation Quality Scoring Evaluation

Figure 17: Inference Speedup on Tesla T4