Dialogue Generation
Leaderboards
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
Greatest papers with code
Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset
ACL 2019
• facebookresearch/ParlAI
• 
One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill.
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.
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.
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
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
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.
Zero-Shot Dialog Generation with Cross-Domain Latent Actions
WS 2018
• snakeztc/NeuralDialog-ZSDG
•
This paper introduces zero-shot dialog generation (ZSDG), as a step towards neural dialog systems that can instantly generalize to new situations with minimal data.