https://github.com/swisscom/churn-intent-DE
공식 코드Churn Intent Detection in Multilingual Chatbot Conversations and Social Media
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Intent Detection is a task of determining the underlying purpose or goal behind a user's search query given a context. The task plays a significant role in search and recommendations. A traditional approach for intent detection implies using an intent detector model to classify user search query into predefined intent categories, given a context. One of the key challenges of the task implies identifying user intents for cold-start sessions, i.e., search sessions initiated by a non-logged-in or unrecognized user.
Source: Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers <span class="description-source">
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Churn Intent Detection in Multilingual Chatbot Conversations and Social Media
ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning
Metric Learning and Adaptive Boundary for Out-of-Domain Detection
Deep Open Intent Classification with Adaptive Decision Boundary
TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition
TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition
Generate then Refine: Data Augmentation for Zero-shot Intent Detection
RSVP: Customer Intent Detection via Agent Response Contrastive and Generative Pre-Training
Coupling Intent and Action for Pedestrian Crossing Behavior Prediction
Multi-Tenant Optimization For Few-Shot Task-Oriented FAQ Retrieval
MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention
From Disfluency Detection to Intent Detection and Slot Filling
Energy-based Unknown Intent Detection with Data Manipulation
CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding
A Persian Benchmark for Joint Intent Detection and Slot Filling
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.