https://github.com/leob03/Image_captionning
Neural Machine Translation by Jointly Learning to Align and Translate
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.
Neural Machine Translation by Jointly Learning to Align and Translate
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
Neural Machine Translation by Jointly Learning to Align and Translate
Multiple Generative Models Ensemble for Knowledge-Driven Proactive Human-Computer Dialogue Agent
Neural Machine Translation by Jointly Learning to Align and Translate
Wizard of Wikipedia: Knowledge-Powered Conversational agents
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
Neural Machine Translation by Jointly Learning to Align and Translate
FaithDial: A Faithful Benchmark for Information-Seeking Dialogue
G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment
BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining
Neural Machine Translation by Jointly Learning to Align and Translate
Neural Machine Translation by Jointly Learning to Align and Translate
MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation
Neural Machine Translation by Jointly Learning to Align and Translate
Neural Machine Translation by Jointly Learning to Align and Translate
Neural Machine Translation by Jointly Learning to Align and Translate
Neural Machine Translation by Jointly Learning to Align and Translate
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
Neural Machine Translation by Jointly Learning to Align and Translate
Neural Machine Translation by Jointly Learning to Align and Translate
Importance of Search and Evaluation Strategies in Neural Dialogue Modeling
End-to-end Adversarial Learning for Generative Conversational Agents
Personalizing Dialogue Agents: I have a dog, do you have pets too?
Neural Machine Translation by Jointly Learning to Align and Translate
Personalizing Dialogue Agents: I have a dog, do you have pets too?
Neural Machine Translation by Jointly Learning to Align and Translate
Neural Machine Translation by Jointly Learning to Align and Translate
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
Neural Machine Translation by Jointly Learning to Align and Translate
Another Diversity-Promoting Objective Function for Neural Dialogue Generation
Neural Machine Translation by Jointly Learning to Align and Translate
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.