Open-Domain Dialog
32 papers with code • 1 benchmarks • 11 datasets
Datasets
Latest papers with no code
Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations
Open-domain dialog involves generating search queries that help obtain relevant knowledge for holding informative conversations.
Enhancing Task Bot Engagement with Synthesized Open-Domain Dialog
To better mimic human-level conversations that usually fuse various dialog modes, it is essential to build a system that can effectively handle both TOD and ODD and access different knowledge sources.
Interactive Evaluation of Dialog Track at DSTC9
Our track challenges participants to develop strong response generation models and explore strategies that extend them to back-and-forth interactions with real users.
Towards Learning Through Open-Domain Dialog
The development of artificial agents able to learn through dialog without domain restrictions has the potential to allow machines to learn how to perform tasks in a similar manner to humans and change how we relate to them.
An Empirical Study of Topic Transition in Dialogue
Transitioning between topics is a natural component of human-human dialog.
User Response and Sentiment Prediction for Automatic Dialogue Evaluation
Automatic evaluation is beneficial for open-domain dialog system development.
Investigating the Impact of Pre-trained Language Models on Dialog Evaluation
Yet, the impact of different Pr-LMs on the performance of automatic metrics is not well-understood.
Enhancing Self-Disclosure In Neural Dialog Models By Candidate Re-ranking
One such area is of open-domain dialog modelling, neural dialog models based on GPT-2 such as DialoGPT have shown promising performance in single-turn conversation.
REAM$\sharp$: An Enhancement Approach to Reference-based Evaluation Metrics for Open-domain Dialog Generation
We first clarify an assumption on reference-based metrics that, if more high-quality references are added into the reference set, the reliability of the metric will increase.
Discovering Dialog Structure Graph for Open-Domain Dialog Generation
Learning interpretable dialog structure from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dialog generation.