Recent Advances and Challenges in Task-oriented Dialog System
arXiv:2003.07490
Abstract
Due to the significance and value in human-computer interaction and natural language processing, task-oriented dialog systems are attracting more and more attention in both academic and industrial communities. In this paper, we survey recent advances and challenges in task-oriented dialog systems. We also discuss three critical topics for task-oriented dialog systems: (1) improving data efficiency to facilitate dialog modeling in low-resource settings, (2) modeling multi-turn dynamics for dialog policy learning to achieve better task-completion performance, and (3) integrating domain ontology knowledge into the dialog model. Besides, we review the recent progresses in dialog evaluation and some widely-used corpora. We believe that this survey, though incomplete, can shed a light on future research in task-oriented dialog systems.
arXiv commentUnder review of SCIENCE CHINA Technological Science (SCTS)
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Cited by in corpus (8)
- UBAR: Towards Fully End-to-End Task-Oriented Dialog Systems with GPT-2
- A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset
- A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots
- Is Your Goal-Oriented Dialog Model Performing Really Well? Empirical Analysis of System-wise Evaluation
- Augmenting Customer Support with an NLP-based Receptionist
- SGD-QA: Fast Schema-Guided Dialogue State Tracking for Unseen Services
- A Context-Aware Hierarchical BERT Fusion Network for Multi-turn Dialog Act Detection
- Rating Prediction in Conversational Task Assistants with Behavioral and Conversational-Flow Features