This article explains the principles and structures of LSTM networks, introduces the ELMO contextual embedding model with its two‑stage pre‑training and downstream usage, and provides an overview of the Transformer architecture, highlighting their roles in modern NLP tasks.
This article reviews the evolution of language models from bag‑of‑words to BERT, compares ELMo, GPT, and BERT architectures, and details how NetEase Yanxuan applies pre‑trained models to classification, text matching, sequence labeling, and generative tasks in production.
This article reviews the taxonomy of transfer learning in NLP, summarizes representative pre‑training models such as ELMo, ULMFiT, BERT, GPT, MASS and UNILM, discusses their strengths and limitations, and provides practical recommendations for applying these techniques in real‑world projects.
Natural language processing, a cornerstone of AI, relies on language models to capture linguistic features; this article reviews classic pre‑training models—ELMo, GPT, and BERT—explaining their architectures, training objectives, and how they boost downstream NLP tasks despite data‑scarcity challenges.
Cheng Huige’s winning AI Challenger 2018 solution treated fine‑grained Chinese review sentiment as a 20‑aspect multi‑class task, combining a high‑capacity LSTM encoder with self‑attention, word‑and‑character embeddings, simplified ELMo pre‑training, diverse tokenizations and a weighted seven‑model ensemble (including BERT), which together delivered the competition’s top F1 performance.
