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Found 15 papers, 5 papers with code
PLATO-K: Internal and External Knowledge Enhanced Dialogue Generation
Recently, the practical deployment of open-domain dialogue systems has been plagued by the knowledge issue of information deficiency and factual inaccuracy.
Link the World: Improving Open-domain Conversation with Dynamic Spatiotemporal-aware Knowledge
Making chatbots world aware in a conversation like a human is a crucial challenge, where the world may contain dynamic knowledge and spatiotemporal state.
Towards Multi-Turn Empathetic Dialogs with Positive Emotion Elicitation
In this task, the agent conducts empathetic responses along with the target of eliciting the user's positive emotions in the multi-turn dialog.
Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals
For example, users have determined the departure, the destination, and the travel time for booking a flight.
Long Time No See! Open-Domain Conversation with Long-Term Persona Memory
Most of the open-domain dialogue models tend to perform poorly in the setting of long-term human-bot conversations.
PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation
To explore the limit of dialogue generation pre-training, we present the models of PLATO-XL with up to 11 billion parameters, trained on both Chinese and English social media conversations.
DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation
In this paper, we provide a bilingual parallel human-to-human recommendation dialog dataset (DuRecDial 2. 0) to enable researchers to explore a challenging task of multilingual and cross-lingual conversational recommendation.
Discovering Dialog Structure Graph for Coherent Dialog Generation
Learning discrete dialog structure graph from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dialog generation.
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.
Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation
To address the challenge of policy learning in open-domain multi-turn conversation, we propose to represent prior information about dialog transitions as a graph and learn a graph grounded dialog policy, aimed at fostering a more coherent and controllable dialog.
Towards Conversational Recommendation over Multi-Type Dialogs
We propose a new task of conversational recommendation over multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e. g., QA) to a recommendation dialog, taking into account user's interests and feedback.
Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs
Two types of knowledge, triples from knowledge graphs and texts from documents, have been studied for knowledge aware open-domain conversation generation, in which graph paths can narrow down vertex candidates for knowledge selection decision, and texts can provide rich information for response generation.
Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification
We present a novel multi-task attention based neural network model to address implicit discourse relationship representation and identification through two types of representation learning, an attention based neural network for learning discourse relationship representation with two arguments and a multi-task framework for learning knowledge from annotated and unannotated corpora.
