This article examines the challenges of short‑video content tagging and describes iQIYI's multi‑stage evolution from simple text‑only models to sophisticated multimodal architectures that fuse cover images, BERT embeddings, and video frames to improve tag generation accuracy.
iQIYI’s short‑video tagging system tackles multimodal fusion, open‑set and abstract tags by evolving from a text‑only model through cover‑image, BERT‑vector, and video‑frame fusion architectures, enabling automated labeling, personalized recommendation, and semantic search while planning to add OCR, audio, and knowledge‑graph enhancements.
This article systematically reviews the history, core concepts, variants, and practical implementations of attention mechanisms—from early additive and multiplicative forms to self‑attention, multi‑head attention, and recent transformer‑based models—highlighting why attention has become fundamental in modern AI research.
This article presents a Transformer‑based framework that simultaneously generates visually compatible outfits and personalizes recommendations by leveraging multimodal item embeddings and user behavior, achieving significant gains in compatibility prediction, fill‑in‑the‑blank accuracy, and click‑through rate on Alibaba's iFashion platform.
This article introduces KOBE, a knowledge‑based personalized text generation system that leverages Transformer architecture, attribute fusion, and external knowledge graphs to produce fluent, domain‑aware recommendation reasons for e‑commerce products, with a case study on the Spring Festival cloud theme.
This article systematically reviews the evolution of attention mechanisms—from early additive and multiplicative forms to self‑attention and multi‑head variants—explaining their core three‑step framework, key differences, and why they have become essential across NLP, vision, and broader AI applications.
This article reviews the historical development of natural language processing language models, covering expert rule‑based systems, statistical n‑grams, smoothing techniques, neural network models such as NNLM, RNN, word2vec, GloVe, ELMo, and the transformer‑based breakthroughs of GPT, BERT and GPT‑2, and summarizes their impact on modern NLP tasks.
This article surveys the evolution of machine translation from early rule‑based systems to modern neural architectures, explains how translation engines are trained, highlights recent advances such as attention and Transformers, and shares practical experience and current challenges in the field.
This article reviews the challenges of media‑domain named entity recognition, outlines the evolution from rule‑based methods through traditional machine‑learning and deep‑learning models to attention‑based Transformers, and details Sohu’s practical Bi‑LSTM‑CRF system with data‑annotation strategies and performance results.
This article reviews the history of natural language processing, explains key deep‑learning models such as NNLM, Word2vec, CNN, RNN, attention mechanisms, and Transformers, and discusses their applications, future trends, and practical considerations in NLP tasks.
This article reviews the current state of machine translation, explores the challenges of ambiguity, quality, and domain specificity, and presents human‑in‑the‑loop translation techniques—including attention‑enhanced models, transformer architectures, and online learning—while discussing practical productization and deployment considerations.
This article explains BERT’s architecture—from its bidirectional Transformer encoder and attention mechanisms to its pre‑training tasks—and presents extensive experiments showing its superior performance on various Chinese and English text‑classification benchmarks across multiple datasets.
This article provides a comprehensive, step‑by‑step explanation of the Transformer architecture, covering its encoder‑decoder structure, self‑attention, multi‑head attention, positional encoding, residual connections, and training processes, illustrated with diagrams and code snippets to aid readers new to neural machine translation.
This article explains how reinforcement learning, specifically the DDPG algorithm combined with Transformer-based networks, is applied to improve large‑scale news recall systems, detailing the business scenario, algorithm selection, model architecture, speed optimizations, training challenges, and observed online performance gains.
The article summarizes a video on Laravel project structuring, explaining how separating responsibilities into layers such as Repository for data access, Service for business logic, Presenter for view preparation, Transformer for data shaping, and Formatter for consistent API responses improves maintainability and scalability.
Researchers introduce a novel suffix‑prediction mechanism for neural machine translation that separately generates stems and suffixes during decoding, dramatically reducing out‑of‑vocabulary errors and morphological mistakes in English‑Russian translation, achieving consistent improvements across RNN and Transformer models on large‑scale news and e‑commerce datasets.
