Intent Detection
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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">
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AaronTengDeChuan/MLWA-Chinese-SLU
공식 코드Injecting Word Information with Multi-Level Word Adapter for Chinese Spoken Language Understanding
https://github.com/Adaxry/CM-Net
공식 코드CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding
https://github.com/AshishMittal/RMLIntent
공식 코드Representation based meta-learning for few-shot spoken intent recognition
https://github.com/F2-Song/PromptSLU
공식 코드A Unified Framework for Multi-intent Spoken Language Understanding with prompting
https://github.com/HYU-NLP/BlendX
공식 코드BlendX: Complex Multi-Intent Detection with Blended Patterns
https://github.com/Kaleidophon/deep-significance
공식 코드https://github.com/LeePleased/StackPropagation-SLU
공식 코드A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding
https://github.com/LooperXX/AGIF
공식 코드AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
https://github.com/Makbari1997/Persian-Atis
공식 코드A Persian Benchmark for Joint Intent Detection and Slot Filling
https://github.com/Makbari1997/VAE-KPCA-HDBSCAN
공식 코드A Hybrid Architecture for Out of Domain Intent Detection and Intent Discovery
https://github.com/MiuLab/Lattice-ELMo
공식 코드Learning Spoken Language Representations with Neural Lattice Language Modeling
https://github.com/MiuLab/Lattice-SLU
공식 코드Learning Spoken Language Representations with Neural Lattice Language Modeling
https://github.com/PengjieRen/CaSE_WISE
공식 코드Wizard of Search Engine: Access to Information Through Conversations with Search Engines
https://github.com/Perevalov/intent_classifier
공식 코드https://github.com/PhoebusSi/CTIR
공식 코드Learning Class-Transductive Intent Representations for Zero-shot Intent Detection
https://github.com/TRUMANCFY/SLIM
공식 코드SLIM: Explicit Slot-Intent Mapping with BERT for Joint Multi-Intent Detection and Slot Filling
https://github.com/VRU-intention/casr
공식 코드Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation
https://github.com/VinAIResearch/JointIDSF
공식 코드https://github.com/ZovanZhou/MLRN
공식 코드https://github.com/abderrahmanskiredj/DarijaBanking
공식 코드https://github.com/abirilab/aras
공식 코드https://github.com/adlnlp/tri-nlu
공식 코드Tri-level Joint Natural Language Understanding for Multi-turn Conversational Datasets
https://github.com/amazon-research/dse
공식 코드Learning Dialogue Representations from Consecutive Utterances
https://github.com/anhtunguyen98/bislu
공식 코드https://github.com/bionlu-coling2024/biomed-ner-intent_detection
공식 코드Intent Detection and Entity Extraction from BioMedical Literature
https://github.com/bowang-rw-02/scl-one-for-four
공식 코드One Stone, Four Birds: A Comprehensive Solution for QA System Using Supervised Contrastive Learning
https://github.com/cambridgeltl/sqatin
공식 코드SQATIN: Supervised Instruction Tuning Meets Question Answering for Improved Dialogue NLU
https://github.com/cgraywang/deepstruct
공식 코드DeepStruct: Pretraining of Language Models for Structure Prediction
https://github.com/czhang99/Capsule-NLU
공식 코드Joint Slot Filling and Intent Detection via Capsule Neural Networks
https://github.com/didi/delta
공식 코드https://github.com/fangyihao/gptaug
공식 코드ChatGPT as Data Augmentation for Compositional Generalization: A Case Study in Open Intent Detection
https://github.com/fanolabs/isointentbert-main
공식 코드https://github.com/google/uncertainty-baselines
공식 코드A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection
https://github.com/gt4sd/zberta
공식 코드https://github.com/gt4sd/zero-shot-bert-adapters
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.