Dialogue Generation
Leaderboards
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
Greatest papers with code
Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation
However, previous work in dialogue response generation has shown that these metrics do not correlate strongly with human judgment in the non task-oriented dialogue setting.
End-to-end Adversarial Learning for Generative Conversational Agents
This paper presents a new adversarial learning method for generative conversational agents (GCA) besides a new model of GCA.
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language tokens.
Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network
Human generates responses relying on semantic and functional dependencies, including coreference relation, among dialogue elements and their context.
CHATBOT DEEP ATTENTION DIALOGUE GENERATION MACHINE TRANSLATION
Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders
ACL 2017
• snakeztc/NeuralDialog-CVAE
• 
While recent neural encoder-decoder models have shown great promise in modeling open-domain conversations, they often generate dull and generic responses.
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations.
Adversarial Learning for Neural Dialogue Generation
EMNLP 2017
• liuyuemaicha/Adversarial-Learning-for-Neural-Dialogue-Generation-in-Tensorflow
•
In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances.
SOTA for Dialogue Generation on Amazon-5
Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation
ACL 2018
• snakeztc/NeuralDialog-LAED
• 
The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains.
DIALOGUE GENERATION DIALOGUE INTERPRETATION REPRESENTATION LEARNING
Deep Reinforcement Learning for Dialogue Generation
EMNLP 2016
• liuyuemaicha/Deep-Reinforcement-Learning-for-Dialogue-Generation-in-tensorflow
•
Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes.
Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models
NAACL 2019
• budzianowski/multiwoz
•
Defining action spaces for conversational agents and optimizing their decision-making process with reinforcement learning is an enduring challenge.
DECISION MAKING DIALOGUE GENERATION DIALOGUE MANAGEMENT GOAL-ORIENTED DIALOGUE SYSTEMS POLICY GRADIENT METHODS