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Response Generation
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A task where an agent should play the $DE$ role and generate a text to respond to a $P$ message.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ancientshi/erm4
https://github.com/anforsm/self-refine
https://github.com/ankita1404/ConversationalChatBot
A Diversity-Promoting Objective Function for Neural Conversation Models
https://github.com/aryasbandopadhyay/Chatbot-rnn
A Diversity-Promoting Objective Function for Neural Conversation Models
https://github.com/aswinapk/chatbot_rnn
A Diversity-Promoting Objective Function for Neural Conversation Models
https://github.com/boru-roylu/theta
DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues
https://github.com/bzantium/posterior-knowledge-selection
Learning to Select Knowledge for Response Generation in Dialog Systems
https://github.com/chncwang/InsNet
Cue-word Driven Neural Response Generation with a Shrinking Vocabulary
https://github.com/ctr4si/A-Hierarchical-Latent-Structure-for-Variational-Conversation-Modeling
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
https://github.com/cui0523/Code6/tree/main/mass
MASS: Masked Sequence to Sequence Pre-training for Language Generation
https://github.com/devJWSong/bert-crf-entity-recognition-pytorch
ReCoSa: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue Generation
https://github.com/dreasysnail/CoCon
Consistent Dialogue Generation with Self-supervised Feature Learning
https://github.com/egillian1/AblationPLATO
PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable
https://github.com/facebookresearch/data2vec_vision
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/facebookresearch/ketod
https://github.com/facebookresearch/simmc2
https://github.com/fadedcosine/pos-guided-neural-text-generation
Improving Neural Response Diversity with Frequency-Aware Cross-Entropy Loss
https://github.com/fate-ubw/raglab
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
https://github.com/fuqiang-git-hub/unilmv1-Paddle
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/gmftbyGMFTBY/HashRetrieval
Group-wise Contrastive Learning for Neural Dialogue Generation
https://github.com/google-research-datasets/dstc8-schema-guided-dialogue
Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset
https://github.com/guxd/DialogWAE
DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder
https://github.com/guxd/dialogbert
DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances
https://github.com/hccngu/metnet
MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling
https://github.com/hkust-knowcomp/knowledge-constrained-decoding
Towards a Unified Multi-Dimensional Evaluator for Text Generation
https://github.com/huggingface/swift-coreml-transformers
https://github.com/huggingface/transformers
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
https://github.com/imcaspar/gpt2-ml
https://github.com/jankrepl/mildlyoverfitted
https://github.com/jiaruncao/BioCopyMechanism
MASS: Masked Sequence to Sequence Pre-training for Language Generation
https://github.com/jimth001/Bi-Seq2Seq
https://github.com/jina-ai/thinkgpt
https://github.com/jojonki/MultiWOZ-Parser
MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling
https://github.com/julianser/hed-dlg-truncated
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
https://github.com/kaistai/semiparametric_token-sequence_co-supervision
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.