Goal-Oriented Dialogue Systems
13 papers with code • 0 benchmarks • 4 datasets
Achieving a pre-defined goal through a dialog.
Benchmarks
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Most implemented papers
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.
Utterance-level Dialogue Understanding: An Empirical Study
Most of these approaches account for the context for effective understanding.
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.
Sequential Dialogue Context Modeling for Spoken Language Understanding
We compare the performance of our proposed architecture with two context models, one that uses just the previous turn context and another that encodes dialogue context in a memory network, but loses the order of utterances in the dialogue history.
NIPS Conversational Intelligence Challenge 2017 Winner System: Skill-based Conversational Agent with Supervised Dialog Manager
We present bot{\#}1337: a dialog system developed for the 1st NIPS Conversational Intelligence Challenge 2017 (ConvAI).
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.
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.
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.
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.
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.