https://github.com/18907305772/maker
공식 코드Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog
Papers with Code 아카이브
Response Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 3
- 보존된 평가 기록
- 10
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/27182812/LLM4ED
공식 코드https://github.com/934865517zjk/csim
공식 코드https://github.com/934865517zjk/simoap
공식 코드https://github.com/AADeLucia/gpt2-narrative-decoding
공식 코드https://github.com/AkariAsai/self-rag
공식 코드Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
https://github.com/Ali-Forootani/multi_llm
공식 코드Bio-Eng-LMM AI Assist chatbot: A Comprehensive Tool for Research and Education
https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/pace
공식 코드PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts
https://github.com/Ariya12138/CORAL
공식 코드CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation
https://github.com/HLTCHKUST/CAiRE_in_DialDoc21
공식 코드CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue System
https://github.com/Hyu-Zhang/ISR
공식 코드Uncovering Hidden Connections: Iterative Search and Reasoning for Video-grounded Dialog
https://github.com/InitialBug/MarCo-Dialog
공식 코드https://github.com/JD-AI-Research-NLP/RevCore
공식 코드https://github.com/Jiahuan-Pei/multiwoz-mdrg
공식 코드Retrospective and Prospective Mixture-of-Generators for Task-oriented Dialogue Response Generation
https://github.com/LynetteXing1991/HRAN
공식 코드Hierarchical Recurrent Attention Network for Response Generation
https://github.com/Maluuba/nlg-eval
공식 코드https://github.com/MarkWuNLP/ResponseEdit
공식 코드https://github.com/NoSyu/SSREM
공식 코드https://github.com/PaddlePaddle/Knover
공식 코드PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning
https://github.com/PaddlePaddle/Research
공식 코드PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable
https://github.com/PaddlePaddle/Research/tree/master/NLP/EMNLP2019-AKGCM
공식 코드Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs
https://github.com/Pascalson/LERG
공식 코드https://github.com/PengjieRen/CaSE-1.0
공식 코드Conversations with Search Engines: SERP-based Conversational Response Generation
https://github.com/PengjieRen/CaSE_WISE
공식 코드Wizard of Search Engine: Access to Information Through Conversations with Search Engines
https://github.com/RUCAIBox/TG-ReDial
공식 코드https://github.com/ReMine-Lab/RRGen
공식 코드https://github.com/SRPOL-AUI/storir
공식 코드StoRIR: Stochastic Room Impulse Response Generation for Audio Data Augmentation
https://github.com/Sahandfer/CEM
공식 코드https://github.com/ShaojieJiang/FACE
공식 코드Improving Neural Response Diversity with Frequency-Aware Cross-Entropy Loss
https://github.com/Silin159/PARG
공식 코드https://github.com/SmartDataAnalytics/KG-Copy_Network
공식 코드https://github.com/SmartDataAnalytics/kgirnet
공식 코드Grounding Dialogue Systems via Knowledge Graph Aware Decoding with Pre-trained Transformers
https://github.com/TobeyYang/S2S_Temp
공식 코드https://github.com/TobeyYang/StyleDGPT
공식 코드StyleDGPT: Stylized Response Generation with Pre-trained Language Models
https://github.com/TonyNemo/UBAR-MultiWOZ
공식 코드UBAR: Towards Fully End-to-End Task-Oriented Dialog Systems with GPT-2
https://github.com/Yui010206/CREMA
공식 코드CREMA: Generalizable and Efficient Video-Language Reasoning via Multimodal Modular Fusion
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.