This tutorial introduces AIGC concepts and walks through practical implementations of tokenization, part‑of‑speech tagging, and named entity recognition using the Transformers library, NLTK, and spaCy on Google Colab, complete with code snippets and visual results.
This article provides a detailed introduction to BERT, covering its bidirectional transformer encoder design, pre‑training objectives such as Masked Language Modeling and Next Sentence Prediction, model configurations, differences from GPT/ELMo, and a wide range of downstream NLP applications.
This article introduces the KANGAROO model, which injects hierarchical semantic information via hyperbolic space and leverages contrastive learning on dense subgraph structures to overcome global sparsity in vertical‑domain knowledge‑enhanced pre‑trained language models, and evaluates its performance on finance and medical tasks.
This article explores the concept of the metaverse and virtual humans, detailing 3D modeling techniques, NLP-driven language understanding, streaming TTS, VR/AR interaction, AIGC content generation, and the deployment of a large‑model intelligent Q&A system with real‑time facial expression synthesis for virtual anchors.
This tutorial demonstrates how to preprocess a Chinese lyrics JSON dataset, extract Jay Chou's songs using Python, perform word segmentation with Jieba, compute word frequencies, and create visualizations such as word clouds both programmatically and with online tools.
This article provides a comprehensive English overview of BERT, covering its original paper, model architecture, pre‑training objectives (Masked Language Model and Next Sentence Prediction), differences from ELMo, GPT and vanilla Transformers, parameter counts, main contributions, and a range of NLP application scenarios such as text classification, sentiment analysis, NER, and machine translation.
Taobao’s Technical Industry Data team designed a four‑level VOC tagging hierarchy to unify fragmented user‑feedback sources, evaluated label similarity with vector‑based distance matrices, optimized tag groups via entropy‑driven re‑grouping, built a stacking ensemble of FastText and TextCNN achieving over 90% accuracy, and deployed an automated production pipeline that generates tags, maintains ODPS tables, and provides APIs for rapid experimentation.
This article provides a comprehensive overview of Google's T5 model, detailing its unified text‑to‑text formulation, encoder‑decoder architecture, three model variants, attention mask designs, training strategies, model sizes, experimental results, and key contributions to natural language processing.
This article presents a comprehensive overview of personalized title generation—covering keyword‑based, click‑sequence‑based, and author‑style‑based methods using transformer and LSTM models—and describes an end‑to‑end pipeline for automatic cover image synthesis that combines image restoration, Seq2Seq key‑phrase extraction, object detection, and layout generation to improve user engagement in information‑flow scenarios.
This article introduces Elasticsearch, explains its hot‑warm‑cold lifecycle management, demonstrates vector search and built‑in NLP capabilities, and describes how the 360 Zhihui Cloud Platform integrates these features with practical test cases and new visualization tools.
This article provides a thorough overview of large language models, explaining the relationship between NLP and LLMs, the evolution from RNN to LSTM, the fundamentals of attention mechanisms, and the architecture and operation of Transformer models, all illustrated with clear examples and diagrams.
This guide demonstrates how to use Python for end‑to‑end text data analysis, covering preprocessing, word‑frequency visualization, classification, sentiment detection, similarity measurement, entity recognition, keyword extraction, summarization, translation, and generation with clear code examples.
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 describes the design and implementation of a Document Analytics & Anti‑Fraud Support platform for Hong Kong virtual banking, detailing its OCR/NLP‑driven pipeline, dynamic rule engine, multi‑template PDF processing, model training, and the resulting improvements in fraud detection and operational efficiency.
Evaluating language models solely with ROUGE or BLEU is insufficient, so comprehensive benchmarks like GLUE, SuperGLUE, MMLU, C‑Eval, and HELM provide diverse tasks and metrics that more accurately assess linguistic understanding, knowledge acquisition, and robustness across English and Chinese NLP systems.
This article introduces word‑level representations—from one‑hot encoding to dense word embeddings via Word2Vec—explains cosine similarity, then walks through the structure, limitations, and PyTorch implementation of a vanilla RNN, including a custom forward function and verification against the library API.
This article provides a comprehensive overview of natural language processing and language‑model research, tracing the historical development from early rule‑based and statistical N‑gram models through neural network approaches such as RNN, LSTM, ELMo, and Transformer, and detailing the architectures, strengths, and limitations of the GPT series up to GPT‑4, while also discussing evaluation metrics, practical applications, and future challenges.
EcomGPT, an Alibaba‑trained e‑commerce large language model, uses a 1.5 million‑sample instruction dataset (EcomInstruct) to demonstrate that domain‑specific instruction tuning dramatically outperforms general‑purpose models on e‑commerce tasks, reducing hallucinations and improving task accuracy, with performance scaling as data diversity increases.
This article presents a comprehensive overview of how natural language processing is applied to financial risk control, covering text and non‑text sequence modeling, tokenization strategies, transformer‑based long‑text architectures, multi‑modal fusion methods, pre‑training techniques and practical sample‑optimization approaches.
This article examines the governance of WeChat's NLP algorithm microservices, outlining the management, performance, and scheduling challenges they face and presenting solutions such as automated CI/CD pipelines, dynamic scaling, DAG‑based service composition, a custom tracing system, the PyInter interpreter, and an improved load‑balancing algorithm.
This article surveys the development of natural language processing from early rule‑based systems through statistical n‑gram models, neural language models, RNNs, LSTMs, ELMo, Transformers and BERT, and then details the architecture, training methods, advantages and limitations of the GPT‑1, GPT‑2, GPT‑3, ChatGPT and GPT‑4 models, concluding with a discussion of future challenges and references.
This tutorial walks you through implementing a full Transformer model in PyTorch, starting from basic linear‑regression code, adding attention mechanisms, multi‑head attention, encoder‑decoder architecture, training loops, and inference, all reinforced with practical debugging tips.
This tutorial shows how to create QR codes, word clouds, batch image segmentation, sentiment analysis, mask detection, simple message spamming, OCR, and a number‑guessing game in Python using no more than ten lines of code for each task, illustrating the power of concise scripting for AI‑related utilities.
While the hype around large language models suggests they are a universal solution, this article examines their high operational costs, slow response times, unnecessary features, legal risks, and compares them with traditional NLP techniques, arguing that they are not a silver bullet but one tool among many.
This article examines ChatGPT's limitations in real‑time information and answer accuracy, then proposes a retrieval‑augmented method that combines up‑to‑date document search with large language models to deliver more reliable and current responses across various scenarios.
LayoutMask introduces a novel multi-modal pre‑training model that replaces global 1D position with local 1D position and adds Whole Word Masking, Layout‑Aware Masking, and Masked Position Modeling, achieving state‑of‑the‑art results on various visually‑rich document understanding tasks.
This article explains GPT's two‑stage learning—unsupervised generative pre‑training on large raw corpora followed by discriminative fine‑tuning on labeled tasks—detailing the underlying Transformer decoder architecture, loss functions, and task‑specific input transformations.
This article introduces user path analysis and the SessionAnalytics open‑source framework, covering business scenarios, data processing techniques, algorithmic mining methods, technical architecture, implementation details, comparisons with event‑based analysis, and a comprehensive Q&A for practical deployment.
This article details HuoLala's AI‑driven intelligent quality inspection system, covering its NLP‑based semantic understanding pipeline, data denoising, confidence learning, contrastive learning, model acceleration techniques such as pruning, knowledge distillation, quantization, and interpretability methods to improve coverage, recall and risk detection.
This article provides a comprehensive overview of the attention mechanism, its mathematical foundations, the transformer model architecture—including encoder and decoder components—and the BERT pre‑training model, detailing their principles, implementations, and applications in natural language processing.
This article introduces user path analysis and the SessionAnalytics open‑source framework, covering business scenarios, technical architecture, data integration, session segmentation, data cleaning, sampling, graph structures, NLP‑based mining, clustering, and visualization techniques for extracting insights from large‑scale user behavior data.
This article introduces the ModelScope community's suite of foundational NLP models—including tokenization, POS tagging, NER, and text representation—detailing their architectures, performance, application scenarios, while also highlighting research contributions such as the ACE framework and retrieval‑enhanced techniques.
Haro’s intelligent customer service combines a smart FAQ recommender and a conversational chatbot that leverages matching‑based intent recognition, large‑scale domain pre‑training, metric‑learning for new intents, and fine‑tuned generative LLMs, achieving 82 % top‑1 accuracy while reducing human workload and outlining future API‑orchestrated, multimodal AI enhancements.
This guide shows how to use Python libraries such as docx, jieba, NLTK, and openpyxl to read a Word file, perform tokenization, compute word frequencies, assign part‑of‑speech tags, and export the results into an Excel spreadsheet, including troubleshooting tips for common errors.
This article shares the development of a global search platform that leverages AI technologies such as Chinese word segmentation, part‑of‑speech tagging, text similarity via Simhash and Synonyms, image similarity using histogram, Hamming distance and ResNet‑50, and multimodal CLIP‑based models to improve search efficiency and accuracy.
The article examines ChatGPT's limitations in timeliness and factual accuracy, especially for security‑related queries, and proposes a method that combines external document search with the model to deliver up‑to‑date, reliable answers across intelligent‑assistant scenarios.
The article introduces COPNER, a contrastive‑learning framework that uses class‑specific prompt words to guide sentence encoders, addressing the limited semantic capture of existing few‑shot NER methods and demonstrating superior performance across multiple benchmark datasets and K‑shot settings.
This article explains how Alibaba's enterprise search system tackles query intent recognition by combining knowledge‑enhanced techniques, short‑text classification, and pretrained language models such as StructBERT and prompt‑learning, and it shares two real‑world case studies, experimental results, and future research directions.
This article reviews the development of event extraction, explains its importance for knowledge graphs, surveys four major algorithmic paradigms, introduces the OmniEvent open‑source toolkit with its unified benchmark and modular design, and outlines future research directions such as document‑level extraction and event relation modeling.
This article explains what ChatGPT is, how it builds on natural-language-processing and the Transformer-based GPT series, details its model-size growth, architectural enhancements, multilingual support, and walks through the tokenization-to-generation pipeline that enables coherent AI-driven conversations.
This article walks through a Python solution for semantic similarity using Word2Vec, showing the problem context, required data preprocessing, the exact code implementation, and the resulting output, while also offering practical tips for asking technical questions in community groups.
This article explains how ChatGPT works by covering the fundamentals of natural language processing, generative language models, deep learning, the Transformer architecture, attention mechanisms, few‑shot learning, and the reinforcement‑learning techniques that align its outputs with human preferences.
This talk reviews the recent trend of unifying natural‑language processing and computer‑vision models through shared transformer architectures, masked‑image‑modeling pre‑training, brain‑inspired prediction mechanisms, and practical benefits such as knowledge sharing, multimodal applications, and cost efficiency, while highlighting the evolution of Swin Transformer and its next‑generation variants.
This article examines the governance of WeChat NLP algorithm microservices, outlining the management, performance, and scheduling challenges they pose, and presents solutions including automated CI/CD pipelines, task‑aware auto‑scaling, DAG‑based service composition, custom Python interpreter PyInter, and an improved Joint‑Idle‑Queue load‑balancing algorithm.
This extensive report examines ChatGPT’s origins, the evolution of artificial intelligence and natural language processing, details the underlying Transformer architecture and GPT series, discusses its limitations, and explores the wide-ranging commercial applications and future prospects of generative AI.
This article provides a comprehensive overview of ChatGPT prompt engineering, covering its background, design principles, step‑by‑step workflow, numerous practical examples—including code generation, entity extraction, and style rewriting—and discusses why prompts are crucial for large language model performance.
This study systematically evaluates ChatGPT and other large language models on their ability to answer commonsense questions, assess their knowledge awareness, and utilize generated knowledge for reasoning, revealing strong QA performance but notable gaps in social and temporal commonsense and in leveraging contextual knowledge.
The article explains that ChatGPT produces responses by converting words into high‑dimensional vectors, feeding them through neural networks, and selecting tokens based on probability distributions, while also contrasting GPT with BERT and describing a related training event.
A collection of Zhihu answers reflects on how the release of GPT‑4 has reshaped NLP research, dividing the community into LLM‑enthusiasts and skeptics, discussing the relevance of parsing, resource‑driven research directions, and the existential challenges faced by researchers.
This article reviews the evolution of NLP training paradigms, explains why prompt learning is needed, defines its core concepts, and surveys major hard‑template and soft‑template methods such as PET, LM‑BFF, P‑tuning, and Prefix‑tuning, highlighting their advantages for few‑shot and zero‑shot scenarios.
This tutorial demonstrates how to use Python libraries such as itchat, jieba, matplotlib, SnowNLP, and Tencent Youtu SDK to collect WeChat friend information and perform data analysis on gender distribution, avatar characteristics, signature text (including word‑cloud and sentiment analysis), and geographic location, presenting the results with visual charts and maps.
This article explains how to overcome OpenAI's token limits by splitting massive insurance documents into manageable chunks, vectorizing them with embeddings, using a custom "broccoli" algorithm for intelligent segmentation, and compressing text with dictionary mapping and tokenization techniques to enable accurate question‑answering via the API.
This article introduces various ChatGPT‑based data‑augmentation techniques for natural language processing, explains how to use prompts for synonym, antonym, homophone, random insertion, deletion, and swapping transformations, and provides concrete example prompts and outputs to illustrate each method.
The article describes how YiduCloud builds real‑world medical knowledge graphs and clinical event graphs from heterogeneous hospital systems (EMR, HIS, LIS, RIS) using data aggregation, de‑identification, quality control, NLP‑driven entity extraction, standardisation, graph construction, cleaning, embedding and various AI‑powered applications such as decision support, intelligent diagnosis, automated medical‑record generation and patient recruitment.
The ACL 2023 Multi‑lingual Document‑grounded Dialogue Competition, hosted by Alibaba DAMO Academy and Nanjing University, introduces the first multilingual document‑dialogue dataset, provides a baseline system, offers a $7,000 prize pool, and invites participants to submit papers to the Doc2dial Workshop for Best Paper awards.
This article reviews the development of ChatGPT from early GPT models, explains its underlying RLHF training, compares it with BERT and GPT‑3, and discusses practical applications such as intelligent writing, customer service, and voice calling, while evaluating performance, cost, and future prospects.
This article reviews the development trends of open‑source NLP products, explains PaddleNLP’s design principles—task‑centric, model‑centric, and solution‑centric—along with its modular, ecosystem‑driven, and production‑ready architecture, and showcases several industry case studies demonstrating its practical applications.
The rapid rise of ChatGPT and AIGC has sparked an intense talent war in China, driving unprecedented demand and salary hikes for NLP and related AI specialists, prompting companies and headhunters to invest heavily in recruiting top researchers and engineers.
Pre‑trained large models are emerging as the foundational infrastructure for AI across industries; this article analyzes their technical advantages, application trends in NLP, CV and multimodal domains, presents a telecom customer‑service case study with performance benchmarks, and outlines future deployment challenges and research directions.
This article presents Tencent AI Lab's intelligent writing assistant, detailing the TexSmart text‑understanding platform, the Effidit writing‑assistant features, a multi‑level controllable unsupervised text‑rewriting method, and a novel ParaScore metric that jointly measures semantic similarity and diversity for paraphrase evaluation.
This guide introduces the challenges in healthcare, explains how artificial intelligence is already reshaping the field, and provides a curated list of recent articles on smart medicine for readers to explore the emerging AI‑healthcare integration.
This article introduces the Transformer architecture, explains the self‑attention mechanism with visual illustrations, and provides a full, runnable PyTorch code example that implements the encoder‑decoder structure for sequence‑to‑sequence tasks.
Xiaohongshu’s end‑to‑end intelligent creative platform extracts high‑quality images, generates diverse titles with RED‑pretrained GPT‑2/T5 models, and selects the best ads using a UCB‑based multi‑armed bandit that balances CTR uplift, revenue and user‑experience, while employing position‑corrected metrics and a scalable dual‑tower DNN to boost long‑tail performance and overall revenue.
In a REDtech live interview, NLP professor Li Lei and Xiaohongshu engineers examined ChatGPT’s strengths—long, topic‑focused replies and few‑shot learning—and its challenges such as hallucinations, safety, lack of real‑time data, model compression, and multimodal AIGC, outlining how the technology could reshape content creation, customer service, and search while requiring careful risk management.
This article explains why ChatGPT performs so well by tracing the evolution of natural‑language processing from rule‑based grammars through statistical n‑gram models to neural architectures like RNNs, LSTMs, attention mechanisms, Transformers, and the massive data and training methods that power modern large language models.
This article presents a log anomaly detection approach that leverages NLP techniques such as Part‑of‑Speech tagging and Named Entity Recognition combined with deep neural networks, detailing a six‑step model, experimental validation on three datasets, and superior performance compared with existing DeepLog and LogClass methods.
This article provides a comprehensive guide to ChatGPT prompt engineering, covering background concepts, design principles, step‑by‑step workflows, diverse use‑case examples, model limitations, and references to key research papers, helping readers craft effective prompts for various NLP tasks.
This article presents a comprehensive overview of industry search, covering its background, core retrieval and ranking technologies—including sparse and dense retrieval, pre‑trained language models, tokenization, NER, adaptive multi‑task training, and re‑ranking models—followed by detailed case studies such as address analysis, family‑ID unification, emergency call handling, education photo‑search, and power‑knowledge‑base integration.
The 2023 AI landscape will be driven by model‑governance reforms, NLP breakthroughs, hyper‑automation, AI‑powered recruiting, metaverse integration, enhanced cybersecurity, and conversational chatbots, all reshaping industries and creating massive economic opportunities worldwide.
This article surveys the evolution of AI‑generated content for intelligent writing, covering its definition, key technologies from RNN Seq2Seq to Transformer‑based models such as UniLM, T5, BART and GPT series, evaluation datasets and metrics, product deployments by Datagrand, and the remaining challenges and future directions.
To boost NetEase Cloud Music’s comment handling, the authors combine active‑learning‑driven relabeling, domain‑specific MLM pretraining, contrastive‑learning‑based sample expansion, and multi‑task BERT sharing to raise sentiment‑classification precision and recall above 90 % and double moderation clean‑rate, while employing prompt‑generated story themes, IP‑focused classifiers, and hot‑word aggregation for effective short‑text topic clustering and scalable, theme‑aware distribution.
Unified Prompt Tuning (UPT) is a meta-learning based few‑shot algorithm that converts diverse NLP tasks into a common Prompt‑Options‑Verbalizer format, enabling large pre‑trained language models to achieve higher accuracy with minimal labeled data, as demonstrated on EMNLP‑2022 benchmarks and SuperGLUE datasets.
The article presents a comprehensive study of POI (point‑of‑interest) recognition and alias linking within travel search, detailing background challenges, a multi‑stage algorithmic framework, extensive offline experiments, and the resulting improvements in online conversion and relevance.
SpanProto, a two‑stage span‑based prototypical network, dramatically improves few‑shot named entity recognition by extracting candidate spans with a global boundary matrix and classifying them via prototypical and margin learning, achieving notable gains on the Few‑NERD benchmark with minimal labeled data.
This article reviews ChatGPT’s rapid rise, compares it with GPT‑3, explains the underlying transformer and reinforcement‑learning‑from‑human‑feedback technologies, outlines the evolution of natural‑language processing, and discusses emerging AI investment opportunities and future trends.
KECP, a Knowledge‑Enhanced Contrastive Prompt‑tuning model, achieves strong few‑shot extractive question answering by converting questions to masked statements, injecting external knowledge via gated fusion, and leveraging contrastive learning alongside masked language modeling, as demonstrated on EMNLP‑2022 benchmarks.
The article explains how combining human expertise with AI techniques—through problem decomposition, model selection, feature engineering, and knowledge‑graph construction—enables practical NLP solutions for document extraction and intelligent Q&A, illustrating the process with contract‑field extraction case studies.
This article reviews NVIDIA's recent work on multi‑role generative dialogue modeling using GPT‑2‑based architectures and on enhancing question‑answering systems with synthetic data pipelines, covering model design, data preparation from Reddit, extensive experiments, scaling effects, and practical Q&A insights.
This presentation reviews the background, significance, current research status, objectives, and key contributions of a graph‑based approach to information extraction, covering entity recognition, relation extraction, event extraction, open‑domain extraction, and the proposed unified modeling framework with experimental results.
This article presents an in‑depth overview of NLP techniques used in voice assistants, covering the end‑to‑end conversational AI pipeline, intent and slot modeling, multi‑turn dialog management, model deployment pipelines, quantization methods, and self‑learning strategies for continuous improvement.
This article explains how Baidu's anti‑spam team tackled large‑scale promotional spam on Baidu Zhidao by combining the Ernie pretrained model with SimCSE contrastive learning, detailing the problem background, traditional methods, text‑representation stages, the SimCSE approach, training pipeline, optimizations, and experimental results.
Prof. Qiu Xipeng’s talk highlighted how large language models can be offered as a service and efficiently adapted via in‑context learning, lightweight label‑tuning, and gradient‑free black‑box optimization, showcasing a unified asymmetric Transformer (CPT) that handles understanding, generation, ABSA and NER tasks while reducing resource demands.
This article explains how a pool‑based active learning framework that combines uncertainty sampling (using BADGE, ALPS, or virtual adversarial perturbations) with diversity‑driven clustering can dramatically cut labeling costs for Transformer‑based NLP models, and presents experimental results showing VAPAL’s competitive performance and early‑stage advantages.
The article outlines a comprehensive knowledge‑base retrieval matching solution—combining PageRank‑enhanced DSL rewriting, keyword and dual‑tower vector recall, contrastive fine‑ranking, and optimized vector‑based ranking—implemented via offline DP training and Sunfish online inference on Milvus, with applications in enterprise search and recommendations and future plans for graph‑neural embeddings.
This article explains how large‑scale NLP models can be paired with smaller models through task‑related and task‑unrelated knowledge distillation, progressive multi‑stage distillation, and reverse distillation, thereby reducing training costs, accelerating inference, and even allowing small models to improve large‑model training via sample‑value assessment.
The article examines the challenge of out‑of‑vocabulary terms in Chinese e‑commerce NLP, reviews classic unsupervised metrics such as frequency, cohesion and neighbor entropy, and proposes a lightweight fully‑convolutional network inspired by image‑segmentation techniques to automatically detect new words.
This article reviews the rapid development of multimodal attribute-level sentiment analysis on social media, outlining its background, defining four core sub‑tasks, summarizing representative recent models—including unified multimodal transformers, coarse‑to‑fine image‑target matching, and vision‑language pre‑training—and discussing experimental results and future research directions.
This article presents Meituan's practical experience with compressing large pre‑trained language models—covering challenges of large‑model deployment, compression techniques such as knowledge distillation, pruning and quantization, the AutoDisc assistant‑model approach, multi‑teacher and iterative distillation, and real‑world applications in search advertising, intelligent assistants, and dual‑tower semantic matching.
This article introduces the Transformer architecture, explains its attention mechanism, details its adaptations for natural language processing, computer vision, and OCR tasks, and presents experimental results of various models such as BERT, ELECTRA, Swin Transformer, and CRNN-BCN on large-scale medical data from Haodf.com.
This article explains how YiduCore processes heterogeneous hospital data (EMR, HIS, LIS, RIS, literature) to construct real‑world medical knowledge graphs and clinical event graphs, detailing pipelines for entity extraction, normalization, graph cleaning, PSR scoring, graph embedding, and showcasing applications such as intelligent diagnosis, question answering, automated medical record generation, and clinical trial patient recruitment.
This tutorial walks you through NLP fundamentals, the evolution of BERT, the concept of pre‑trained models, and a step‑by‑step guide to fine‑tune a Chinese BERT on a cloze‑style task, complete with code snippets and verification results.
This article surveys automatic text summarization, detailing system classifications, extractive, abstractive and hybrid techniques, notable recent research, multi‑document and cross‑lingual challenges, major datasets, evaluation metrics, and promising future research avenues in the field.
In this interview, the chief algorithm expert at Zuoyebang discusses how AI technologies such as NLP, speech recognition, large‑model pre‑training, and knowledge‑graph construction are applied to K‑12 education, covering practical challenges, deployment strategies, and future research directions.
This article provides a comprehensive overview of automatic text summarization, covering extractive, abstractive, and hybrid methods, system classifications, applications, datasets, evaluation metrics, and future research directions within the field of artificial intelligence.
This presentation details the XiaoAi intelligent assistant’s QA system, covering its application scenarios, information extraction techniques (including relation and event extraction with SPO/PSO models), graph‑based question answering methods, cross‑domain slot extraction, path retrieval, and practical Q&A insights.
This article explains the importance of text generation, introduces the PEGASUS model’s gap‑sentence pre‑training for abstractive summarization, and shows how the EasyNLP framework integrates PEGASUS and other Chinese and English summarization models with step‑by‑step installation, data preparation, and training commands.
Graph4NLP is a PyTorch‑ and DGL‑based open‑source library that provides a full pipeline—from static and dynamic graph construction to embedding, learning, prediction, and inference—for applying graph neural networks to a wide range of NLP tasks, with extensive documentation, demos, and future scalability plans.
This article describes the production challenges of using BERT for large‑scale text classification at Zuoyebang, explores lightweight alternatives such as knowledge distillation, pruning and quantization, and details a teacher‑student‑active‑learning pipeline that trains a TextCNN model to match BERT performance while dramatically reducing GPU consumption and improving throughput.
This article presents a comprehensive overview of AI-driven music comment moderation and ranking systems, detailing business scenarios, model architectures, data processing techniques, performance improvements, and future directions for both QQ Music and K‑Song platforms.
This article introduces GAST, a graph‑adaptive semantic transfer framework that combines POS‑based Transformers and hybrid graph attention to improve cross‑domain sentiment analysis, presents related work, details the model architecture, reports extensive experiments showing state‑of‑the‑art results, and discusses future directions.
