CREDIT: Coarse-to-Fine Sequence Generation for Dialogue State Tracking

• URL: http://arxiv.org/abs/2009.10435v1
• Date: Tue, 22 Sep 2020 10:27:18 GMT
• Title: CREDIT: Coarse-to-Fine Sequence Generation for Dialogue State Tracking
• Authors: Zhi Chen, Lu Chen, Zihan Xu, Yanbin Zhao, Su Zhu and Kai Yu
• Abstract summary: A dialogue state tracker aims to accurately find a compact representation of the current dialogue status. We employ a structured state representation and cast dialogue state tracking as a sequence generation problem. Experiments demonstrate our tracker achieves encouraging joint goal accuracy for the five domains in MultiWOZ 2.0 and MultiWOZ 2.1 datasets.
• Score: 44.38388988238695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In dialogue systems, a dialogue state tracker aims to accurately find a compact representation of the current dialogue status, based on the entire dialogue history. While previous approaches often define dialogue states as a combination of separate triples ({\em domain-slot-value}), in this paper, we employ a structured state representation and cast dialogue state tracking as a sequence generation problem. Based on this new formulation, we propose a {\bf C}oa{\bf R}s{\bf E}-to-fine {\bf DI}alogue state {\bf T}racking ({\bf CREDIT}) approach. Taking advantage of the structured state representation, which is a marked language sequence, we can further fine-tune the pre-trained model (by supervised learning) by optimizing natural language metrics with the policy gradient method. Like all generative state tracking methods, CREDIT does not rely on pre-defined dialogue ontology enumerating all possible slot values. Experiments demonstrate our tracker achieves encouraging joint goal accuracy for the five domains in MultiWOZ 2.0 and MultiWOZ 2.1 datasets.

Related papers

• Enhancing Visual Dialog State Tracking through Iterative Object-Entity Alignment in Multi-Round Conversations [3.784841749866846]
  We introduce Multi-round Dialogue State Tracking model (MDST) MDST captures each round of dialog history, constructing internal dialogue state representations defined as 2-tuples of vision-language representations. Experimental results on the VisDial v1.0 dataset demonstrate that MDST achieves a new state-of-the-art performance in generative setting.
  arXiv  Detail & Related papers  (2024-08-13T08:36:15Z)
• DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarization [127.714919036388]
  DIONYSUS is a pre-trained encoder-decoder model for summarizing dialogues in any new domain. Our experiments show that DIONYSUS outperforms existing methods on six datasets.
  arXiv  Detail & Related papers  (2022-12-20T06:21:21Z)
• SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding [68.94808536012371]
  We propose a tree-structured pre-trained conversation model, which learns dialog representations from limited labeled dialogs and large-scale unlabeled dialog corpora. Our method can achieve new state-of-the-art results on the DialoGLUE benchmark consisting of seven datasets and four popular dialog understanding tasks.
  arXiv  Detail & Related papers  (2022-09-14T13:42:50Z)
• OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue [40.62090743056549]
  This paper presents an ontology-aware pretrained language model (OPAL) for end-to-end task-oriented dialogue (TOD) Unlike chit-chat dialogue models, task-oriented dialogue models fulfill at least two task-specific modules: dialogue state tracker (DST) and response generator (RG)
  arXiv  Detail & Related papers  (2022-09-10T04:38:27Z)
• In-Context Learning for Few-Shot Dialogue State Tracking [55.91832381893181]
  We propose an in-context (IC) learning framework for few-shot dialogue state tracking (DST) A large pre-trained language model (LM) takes a test instance and a few annotated examples as input, and directly decodes the dialogue states without any parameter updates. This makes the LM more flexible and scalable compared to prior few-shot DST work when adapting to new domains and scenarios.
  arXiv  Detail & Related papers  (2022-03-16T11:58:24Z)
• Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking [16.07100713414678]
  Few-shot dialogue state tracking (DST) is a realistic solution to this problem. We propose to reformulate dialogue state tracking as a dialogue summarization problem.
  arXiv  Detail & Related papers  (2022-03-03T07:54:09Z)
• Structure Extraction in Task-Oriented Dialogues with Slot Clustering [94.27806592467537]
  In task-oriented dialogues, dialogue structure has often been considered as transition graphs among dialogue states. We propose a simple yet effective approach for structure extraction in task-oriented dialogues.
  arXiv  Detail & Related papers  (2022-02-28T20:18:12Z)
• Dialogue State Tracking with Multi-Level Fusion of Predicted Dialogue States and Conversations [2.6529642559155944]
  We propose the Dialogue State Tracking with Multi-Level Fusion of Predicted Dialogue States and Conversations network. This model extracts information of each dialogue turn by modeling interactions among each turn utterance, the corresponding last dialogue states, and dialogue slots.
  arXiv  Detail & Related papers  (2021-07-12T02:30:30Z)
• Multi-Domain Dialogue State Tracking based on State Graph [23.828348485513043]
  We investigate the problem of multi-domain Dialogue State Tracking (DST) with open vocabulary. Existing approaches usually previous dialogue state with dialogue history as the input to a bi-directional Transformer encoder. We propose to construct a dialogue state graph in which domains, slots and values from the previous dialogue state are connected properly.
  arXiv  Detail & Related papers  (2020-10-21T16:55:18Z)
• Non-Autoregressive Dialog State Tracking [122.2328875457225]
  We propose a novel framework of Non-Autoregressive Dialog State Tracking (NADST) NADST can factor in potential dependencies among domains and slots to optimize the models towards better prediction of dialogue states as a complete set rather than separate slots. Our results show that our model achieves the state-of-the-art joint accuracy across all domains on the MultiWOZ 2.1 corpus.
  arXiv  Detail & Related papers  (2020-02-19T06:39:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.