Intent classification

DataFunTalk

Feb 18, 2021 · Artificial Intelligence

Didi Voice Interaction: ASR Error Correction, Intent Classification, and NER Techniques

This article presents Didi's voice interaction platform, detailing the natural language understanding pipeline, ASR error correction methods, intent classification strategies, and named entity recognition models, while discussing practical deployments, performance gains, and future research directions.

ASR correctionDeep LearningIntent classification

0 likes · 18 min read

Didi Voice Interaction: ASR Error Correction, Intent Classification, and NER Techniques

Didi Tech

Nov 18, 2020 · Artificial Intelligence

Didi Speech Interaction: ASR Error Correction, Intent Classification, and NER Techniques

Didi’s voice‑interaction platform combines a three‑stage ASR error‑correction pipeline, optimized intent‑classification models (both end‑to‑end and retrieval‑based), and advanced Chinese NER using Bi‑GRU‑CRF and BERT‑CRF, boosting transcription accuracy and overall dialogue success while supporting scalable deployment and future enhancements such as lattice inputs and richer acoustic signals.

ASR correctionDeep LearningIntent classification

0 likes · 21 min read

Didi Speech Interaction: ASR Error Correction, Intent Classification, and NER Techniques

58 Tech

Jun 15, 2020 · Artificial Intelligence

Intelligent Voice Robot Architecture, Core Technologies, and Enterprise Applications

This article presents the engineering architecture of intelligent voice robots, detailing voice preprocessing, intent recognition, slot extraction, dialogue management, and showcases multiple enterprise use cases that improve efficiency and revenue across sales, customer service, and recruitment.

Dialogue ManagementIntent classificationSpeech Recognition

0 likes · 14 min read

Intelligent Voice Robot Architecture, Core Technologies, and Enterprise Applications

58 Tech

Nov 18, 2019 · Artificial Intelligence

Comprehensive Solution for Human‑Machine Voice Dialogue Robot at 58.com

This article presents a complete solution for 58.com’s human‑machine voice dialogue robot, detailing its background, overall architecture, intelligent outbound process, core functions such as call service, anti‑spam, status recognition, multi‑turn dialogue management, intent classification, slot extraction, whole‑round intent detection, and various practical application scenarios.

AIDialogue ManagementIntent classification

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Comprehensive Solution for Human‑Machine Voice Dialogue Robot at 58.com

Tencent Cloud Developer

Jul 19, 2019 · Artificial Intelligence

Multi-turn Dialogue Intent Classification: Data Processing, Model Construction, and Operational Optimization

The article details a multi‑turn dialogue intent classification pipeline that extracts and expands labeled utterances, preprocesses text with custom tokenization, trains a two‑layer CNN‑Highway and a multi‑head self‑attention model, analyzes errors, and achieves up to 98.7% accuracy on a large, balanced dataset.

BERTCNNIntent classification

0 likes · 15 min read

Multi-turn Dialogue Intent Classification: Data Processing, Model Construction, and Operational Optimization

Tencent Cloud Developer

Dec 21, 2018 · Artificial Intelligence

Tencent Xiaowei Conversational AI Platform: Architecture, Models, and Applications

Tencent Xiaowei is an open, easy‑to‑integrate conversational AI platform that combines NLU, dialogue management and generation, supports multi‑turn context via Memory Networks, uses bidirectional RNN and CNN‑based intent classifiers, and powers smart speakers, TVs and customer‑service bots by leveraging Tencent’s rich content ecosystem.

Intent classificationNLPTencent Xiaowei

0 likes · 11 min read

Tencent Xiaowei Conversational AI Platform: Architecture, Models, and Applications

Beike Product & Technology

Dec 6, 2018 · Artificial Intelligence

Designing and Deploying a Real‑Estate Dialogue System: Architecture, Challenges, and Practices

The talk outlines how Beike built a real‑estate conversational AI platform, covering the market need for dialogue systems, the five technical challenges, data‑driven intent and slot extraction, model choices such as FastText and Bi‑LSTM‑CRF, a three‑layer system architecture, multi‑intent handling, and future directions like 4D viewing and an internal AI dialogue platform.

BILSTM-CRFIntent classificationNLP

0 likes · 26 min read

Designing and Deploying a Real‑Estate Dialogue System: Architecture, Challenges, and Practices