https://github.com/lyy1994/plm_as_kb
공식 코드Eliciting Knowledge from Large Pre-Trained Models for Unsupervised Knowledge-Grounded Conversation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Dialogue generation is the task of "understanding" natural language inputs - within natural language processing in order to produce output. The systems are usually intended for conversing with humans, for instance back and forth dialogue with a conversation agent like a chatbot. Some example benchmarks for this task (see others such as Natural Language Understanding) include FusedChat and Ubuntu DIalogue Corpus (UDC). Models can be evaluated via metrics such as BLEU, ROUGE, and METEOR albeit with challenges in terms of weak correlation with human judgement, that may be addressed by new ones like UnSupervised and Reference-free (USR) and Metric for automatic Unreferenced dialog evaluation (MaUde).
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Eliciting Knowledge from Large Pre-Trained Models for Unsupervised Knowledge-Grounded Conversation
An Empirical Study of Multitask Learning to Improve Open Domain Dialogue Systems
An Equal-Size Hard EM Algorithm for Diverse Dialogue Generation
Controllable Mixed-Initiative Dialogue Generation through Prompting
PromptCBLUE: A Chinese Prompt Tuning Benchmark for the Medical Domain
A Unified Data Augmentation Framework for Low-Resource Multi-Domain Dialogue Generation
SalesBot: Transitioning from Chit-Chat to Task-Oriented Dialogues
An EcoSage Assistant: Towards Building A Multimodal Plant Care Dialogue Assistant
DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling
Elastic Weight Removal for Faithful and Abstractive Dialogue Generation
Knowledge Enhanced Fine-Tuning for Better Handling Unseen Entities in Dialogue Generation
A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation
G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment
End-to-end Adversarial Learning for Generative Conversational Agents
End-to-end Adversarial Learning for Generative Conversational Agents
CausalDialogue: Modeling Utterance-level Causality in Conversations
InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning
EmpDG: Multiresolution Interactive Empathetic Dialogue Generation
Stateful Memory-Augmented Transformers for Efficient Dialogue Modeling
Mind the Gap Between Conversations for Improved Long-Term Dialogue Generation
Emotion-Aware Transformer Encoder for Empathetic Dialogue Generation
DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation
Parameter-Efficient Conversational Recommender System as a Language Processing Task
Text Generation from Knowledge Graphs with Graph Transformers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.