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Recent Advances and Challenges in Task-oriented Dialog System

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arxiv 2003.07490 v3 pith:XKE4ATLJ submitted 2020-03-17 cs.CL

classification cs.CL
keywords dialogtask-orientedsystemsrecentadvanceschallengesmodelingsurvey
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Balancing Knowledge Delivery and Emotional Comfort in Healthcare Conversational Systems

    cs.CL 2025-06 conditional novelty 4.0 of 10

    Fine-tuning a 1B medical chatbot on LLM-rewritten emotional dialogues improves its emotion scores with only small changes in n-gram overlap with the original medical responses.

  2. Building Task Bots with Self-learning for Enhanced Adaptability, Extensibility, and Factuality

    cs.CL 2025-08 conditional novelty 2.0 of 10

    A thesis that combines self-learning from dialog logs, schema-guided prompting, and self-aligned factuality to build task bots with minimal human intervention.

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