Boost NLP Data Quality with Multi‑Stage Back‑Translation Augmentation

This article explains the core principles, implementation steps, and practical challenges of using multi‑language back‑translation to enrich text data, provides Python code for a configurable augmentation pipeline, showcases e‑commerce and financial use cases, and presents evaluation metrics that demonstrate significant gains in semantic fidelity and model performance.

NLPPythonText Generation

0 likes · 9 min read

Boost NLP Data Quality with Multi‑Stage Back‑Translation Augmentation

DataFunTalk

Jan 10, 2021 · Artificial Intelligence

Didi's Machine Translation System: Architecture, Techniques, and WMT2020 Competition Experience

This article presents a comprehensive overview of Didi's machine translation platform, covering its evolution from statistical to neural models, the Transformer architecture with relative position and larger FFN, data preparation, training strategies such as back‑translation and knowledge distillation, deployment optimizations with TensorRT, and the team's successful participation in the WMT2020 news translation task.

BLEUNeural NetworksTensorRT

0 likes · 14 min read

Didi's Machine Translation System: Architecture, Techniques, and WMT2020 Competition Experience

Didi Tech

Oct 27, 2020 · Artificial Intelligence

Didi's Machine Translation System: Architecture, Techniques, and WMT2020 Competition Experience

Didi's machine translation system combines a Transformer‑big architecture with relative position representations, enlarged feed‑forward networks, iterative back‑translation, knowledge‑distillation and domain fine‑tuning, optimized via TensorRT for speed, achieving a BLEU 36.6 and third place in the WMT2020 Chinese‑to‑English news task.

BLEUNeural NetworksTensorRT

0 likes · 15 min read