https://github.com/kennethjones17/chatbot
A Diversity-Promoting Objective Function for Neural Conversation Models
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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/kingsaint/Wikidata-Descriptions
https://github.com/kkxkkx/tensorflow_caption
Towards Building Large Scale Multimodal Domain-Aware Conversation Systems
https://github.com/lemon234071/clean-dialog
DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
https://github.com/llm-lab-org/CLASP
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
https://github.com/lvyufeng/bert4ms
https://github.com/madaan/self-refine
https://github.com/manu-sk/rnn_chatbot
A Diversity-Promoting Objective Function for Neural Conversation Models
https://github.com/michael-wzhu/mpnet_zh
MASS: Masked Sequence to Sequence Pre-training for Language Generation
https://github.com/microsoft/DialogLSP
DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
https://github.com/microsoft/MPNet
MASS: Masked Sequence to Sequence Pre-training for Language Generation
https://github.com/mike-n-7/ADEM
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
https://github.com/milmor/GPT
https://github.com/mindspore-ai/models/blob/master/research/nlp/gpt2
https://github.com/mindspore-ai/models/tree/master/official/nlp/mass
MASS: Masked Sequence to Sequence Pre-training for Language Generation
https://github.com/minimaxir/gpt-2-simple
https://github.com/natashamjaques/neural_chat
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
https://github.com/neeleshnegi/chat-bot
A Diversity-Promoting Objective Function for Neural Conversation Models
https://github.com/openai/gpt-2
https://github.com/overwindows/PALM
PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation
https://github.com/para-zhou/dstc9-posttraining
Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access
https://github.com/pender/chatbot-rnn
A Diversity-Promoting Objective Function for Neural Conversation Models
https://github.com/pengbaolin/soloist
SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching
https://github.com/piekey1994/IOM
A Diversity-Promoting Objective Function for Neural Conversation Models
https://github.com/qbxlvnf11/MultiWOZ2.1-Parser
MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling
https://github.com/ramanakshay/nanogpt
https://github.com/robinsongh381/unilm_pytorch_korean
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/ruc-nlpir/flashrag
Compressing Context to Enhance Inference Efficiency of Large Language Models
https://github.com/rucaibox/tg_crs_code
https://github.com/ruckbreasoning/glm-dialog
PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning
https://github.com/ruiyiw/VT-summ
https://github.com/ryanzhumich/sparc_atis_pytorch
https://github.com/s-nlp/sgdd-tst
Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset
https://github.com/scir-hi/huatuo-llama-med-chinese
https://github.com/shikib/usr
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
https://github.com/shrebox/Personified-Chatbot-I-am-Kalam
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.