https://github.com/ivan-bilan/tac-self-attention
Position-aware Self-attention with Relative Positional Encodings for Slot Filling
Papers with Code 아카이브
Slot Filling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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/joe-prog/https-github.com-facebookresearch-ParlAI
https://github.com/kehanlu/Nemo
Efficient Sequence Transduction by Jointly Predicting Tokens and Durations
https://github.com/lefteris12/multitask-schema-guided-dst
A Multi-Task BERT Model for Schema-Guided Dialogue State Tracking
https://github.com/mahdiabdollahpour/WNUT_2020_sharedtask3
Extracting a Knowledge Base of COVID-19 Events from Social Media
https://github.com/mangushev/intent_slot
https://github.com/megagonlabs/ruler
https://github.com/mgalley/DSTC7-End-to-End-Conversation-Modeling
https://github.com/microsoft/vert-papers/tree/master/papers/DecomposedMetaSL
https://github.com/mindslab-ai/pnlp-mixer
https://github.com/monologg/JointBERT
https://github.com/pengshuang/Joint-Slot-Filling
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
https://github.com/phuongnm94/JointBERT_CAE
https://github.com/princeton-nlp/SimCSE
https://github.com/ray075hl/Bi-Model-Intent-And-Slot
A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling
https://github.com/rita-nlp/italic
https://github.com/rparkin1/intent_LSTM
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
https://github.com/s-nlp/sgdd-tst
Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset
https://github.com/skoltech-nlp/sgdd-tst
Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset
https://github.com/smxiao/GISCo
https://github.com/sonos/svc-demographic-bias-assessment
https://github.com/stonybrooknlp/musique
https://github.com/sxjscience/GluonNLP-Slot-Filling
https://github.com/threelittlemonkeys/pointer-network-pytorch
https://github.com/voicy-ai/DialogStateTracking
https://github.com/wd929/NeMo
Efficient Sequence Transduction by Jointly Predicting Tokens and Durations
https://github.com/yinghao1019/Joint_learn
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
https://github.com/young1993/ugen
https://github.com/ysglh/Task-Oriented-Dialogue-Dataset-Survey
https://github.com/zhoucz97/JointBERT-paddle
https://github.com/zhuzhicai/SQuAD2.0-Baseline-Test-with-BiDAF-No-Answer
https://github.com/zouharvi/kb-shrink
https://gitlab.com/Jaco-Assistant/Jaco-Master
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.