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Dialogue State Tracking
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Dialogue state tacking consists of determining at each turn of a dialogue the full representation of what the user wants at that point in the dialogue, which contains a goal constraint, a set of requested slots, and the user's dialogue act.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AuCson/SEDST
Explicit State Tracking with Semi-Supervision for Neural Dialogue Generation
https://github.com/GaneshGS/data-and-models
https://github.com/KLUE-benchmark/KLUE-baseline
https://github.com/MS-P3/code5/tree/main/gpt2
https://github.com/MoxinC/DST-SC
https://github.com/NVIDIA/NeMo
A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/gpt
https://github.com/ShuoZhangXJTU/PEDP
Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset
https://github.com/VachanVY/gpt.jax
https://github.com/aananda-giri/gpt2-nepali
https://github.com/abhaskumarsinha/Corpus2GPT
https://github.com/abhaskumarsinha/MinimalGPT
https://github.com/affjljoo3581/GPT2
https://github.com/ai-agi/hicl
https://github.com/akanyaani/gpt-2-tensorflow2.0
https://github.com/akanyaani/minGPTF
https://github.com/alexa/alexa-with-dstc10-track2-dataset
"How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations
https://github.com/alibabaresearch/damo-convai
Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing
https://github.com/amazon-science/tanl
Structured Prediction as Translation between Augmented Natural Languages
https://github.com/amolk/editsql
Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions
https://github.com/bcaitech1/p3-dst-chatting-day
Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems
https://github.com/devjwsong/t5-dst-modified-pytorch
https://github.com/fighting41love/funNLP
CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset
https://github.com/huggingface/swift-coreml-transformers
https://github.com/huggingface/transformers
https://github.com/hyan5/learning_to_simulate_nl_feedback
Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions
https://github.com/iflytek/cino
https://github.com/imcaspar/gpt2-ml
https://github.com/jankrepl/mildlyoverfitted
https://github.com/jeongukjae/KR-BERT-SimCSE
https://github.com/kolk/MODELING-ASR-AMBIGUITY-FOR-NEURAL-DIALOGUE-STATE-TRACKING-USING-WORD-CONFUSION-NETWORKS
https://github.com/lefteris12/multitask-schema-guided-dst
A Multi-Task BERT Model for Schema-Guided Dialogue State Tracking
https://github.com/lexmen318/metaCAT
https://github.com/lizi-git/MMConv
https://github.com/lrank/Linguistic_adversity
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.