https://github.com/1053399472/CAISandSMP
CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding
Papers with Code 아카이브
Slot Filling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 14
- 보존된 평가 기록
- 110
The goal of Slot Filling is to identify from a running dialog different slots, which correspond to different parameters of the user’s query. For instance, when a user queries for nearby restaurants, key slots for location and preferred food are required for a dialog system to retrieve the appropriate information. Thus, the main challenge in the slot-filling task is to extract the target entity.
Source: Real-time On-Demand Crowd-powered Entity Extraction
Image credit: Robust Retrieval Augmented Generation for Zero-shot Slot Filling
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/DSInCenter/Persian-Atis
A Persian Benchmark for Joint Intent Detection and Slot Filling
https://github.com/DSKSD/RNN-for-Joint-NLU
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
https://github.com/DSTC-MSR-NLP/DSTC7-End-to-End-Conversation-Modeling
https://github.com/Delta-ML/delta
https://github.com/Domanjiri/joint-bert-with-tf2
https://github.com/Fireblossom/DeepDarkHomeword
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
https://github.com/Fireblossom/DeepDarkHomework
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
https://github.com/HazyResearch/metal
https://github.com/HazyResearch/snorkel
https://github.com/Huawei-MRC-OSI/mrc-nlp-public
https://github.com/MahmoudWahdan/dialog-nlu
https://github.com/MetaronWang/StackPropagation-SLU-TF
A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding
https://github.com/MiuLab/SlotGated-SLU
https://github.com/Polly42Rose/SiriusIntentPredictionSlotFilling
https://github.com/SJY8460/Uni-MIS
https://github.com/ShawonAshraf/nlu-jointbert-dl2021
https://github.com/ShuoZhangXJTU/PEDP
Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset
https://github.com/ZephyrChenzf/SF-ID-Network-For-NLU
A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling
https://github.com/adlnlp/Midas
MIDAS: Multi-level Intent, Domain, And Slot Knowledge Distillation for Multi-turn NLU
https://github.com/ai4bharat/indicbert
https://github.com/alibaba-damo-academy/spokennlp
https://github.com/asadovsky/nn
https://github.com/autonlab/KeyClass
https://github.com/bhchoi/bert-for-joint-ic-sf
https://github.com/birshert/CodeSwitchingAdversarial
Call Larisa Ivanovna: Code-Switching Fools Multilingual NLU Models
https://github.com/bo-ke/cybo/tree/master/tutorials/slu/slot_gated
https://github.com/candoz/memn2n-dialog-babi-keras
https://github.com/chaitjo/personalized-dialog
https://github.com/chimechallenge/C8DASR-Baseline-NeMo
Efficient Sequence Transduction by Jointly Predicting Tokens and Durations
https://github.com/dmis-lab/gener
https://github.com/dsindex/iclassifier
https://github.com/facebookresearch/ParlAI
https://github.com/facebookresearch/editeval
https://github.com/gcucurull/visual-compatibility
https://github.com/google-research-datasets/dstc8-schema-guided-dialogue
Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.