Goal-Oriented Dialogue Systems
12 papers with code • 0 benchmarks • 4 datasets
Achieving a pre-defined goal through a dialog.
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Latest papers
In-Context Learning User Simulators for Task-Oriented Dialog Systems
This paper presents a novel application of large language models in user simulation for task-oriented dialog systems, specifically focusing on an in-context learning approach.
Learning Dialogue Representations from Consecutive Utterances
In this paper, we introduce Dialogue Sentence Embedding (DSE), a self-supervised contrastive learning method that learns effective dialogue representations suitable for a wide range of dialogue tasks.
Knowledge-Driven Slot Constraints for Goal-Oriented Dialogue Systems
Practically, some combinations of slot values can be invalid according to external knowledge.
Maintaining Common Ground in Dynamic Environments
Common grounding is the process of creating and maintaining mutual understandings, which is a critical aspect of sophisticated human communication.
On the Robustness of Intent Classification and Slot Labeling in Goal-oriented Dialog Systems to Real-world Noise
In this work, we investigate how robust IC/SL models are to noisy data.
Grounding Dialogue Systems via Knowledge Graph Aware Decoding with Pre-trained Transformers
Generating knowledge grounded responses in both goal and non-goal oriented dialogue systems is an important research challenge.
Utterance-level Dialogue Understanding: An Empirical Study
Most of these approaches account for the context for effective understanding.
A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset
Dialog State Tracking (DST) is one of the most crucial modules for goal-oriented dialogue systems.
Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-Task Learning
Since such models can greatly benefit from user intent and knowledge graph integration, in this paper we propose an RNN-based end-to-end encoder-decoder architecture which is trained with joint embeddings of the knowledge graph and the corpus as input.
Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models
Defining action spaces for conversational agents and optimizing their decision-making process with reinforcement learning is an enduring challenge.