https://bitbucket.org/ZimingLi/dg-irl-aaai2019
공식 코드Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning
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.
Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning
On the Encoder-Decoder Incompatibility in Variational Text Modeling and Beyond
Bilateral Personalized Dialogue Generation with Contrastive Learning
The StatCan Dialogue Dataset: Retrieving Data Tables through Conversations with Genuine Intents
PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation
PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable
Long Time No See! Open-Domain Conversation with Long-Term Persona Memory
Improving Conditional Sequence Generative Adversarial Networks by Stepwise Evaluation
DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs
Can LLMs Simulate L2-English Dialogue? An Information-Theoretic Analysis of L1-Dependent Biases
OpenViDial: A Large-Scale, Open-Domain Dialogue Dataset with Visual Contexts
Defending Against Backdoor Attacks in Natural Language Generation
You Impress Me: Dialogue Generation via Mutual Persona Perception
Grounding Dialogue Systems via Knowledge Graph Aware Decoding with Pre-trained Transformers
SLIDE: Integrating Speech Language Model with LLM for Spontaneous Spoken Dialogue Generation
VisTai: Benchmarking Vision-Language Models for Traditional Chinese in Taiwan
Automatically Learning Data Augmentation Policies for Dialogue Tasks
Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts
HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation
An Equal-Size Hard EM Algorithm for Diverse Dialogue Generation
SAGE: Steering and Refining Dialog Generation with State-Action Augmentation
Long-term Control for Dialogue Generation: Methods and Evaluation
NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes
Improving Medical Dialogue Generation with Abstract Meaning Representations
We are what we repeatedly do: Inducing and deploying habitual schemas in persona-based responses
Semantic Space Grounded Weighted Decoding for Multi-Attribute Controllable Dialogue Generation
Pretrained Language Models for Dialogue Generation with Multiple Input Sources
A Model-Agnostic Data Manipulation Method for Persona-based Dialogue Generation
Towards Efficiently Diversifying Dialogue Generation via Embedding Augmentation
Towards Robust Personalized Dialogue Generation via Order-Insensitive Representation Regularization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.