Dialogue Generation

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).

Benchmarks

Libraries

Datasets

Subtasks

Most implemented papers

Personalizing Dialogue Agents: I have a dog, do you have pets too?

Content

TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents

Neural Machine Translation by Jointly Learning to Align and Translate

Deep Reinforcement Learning for Dialogue Generation

Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset

Adversarial Learning for Neural Dialogue Generation

MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations

NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist

Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation

Multiple Generative Models Ensemble for Knowledge-Driven Proactive Human-Computer Dialogue Agent