mlsanityData to 14 Oct 2026

Schema Encoding for Transferable Dialogue State Tracking

arXiv 2210.02351 ↗First posted 5 Oct 2022cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

Dialogue state tracking (DST) is an essential sub-task for task-oriented dialogue systems. Recent work has focused on deep neural models for DST. However, the neural models require a large dataset for training. Furthermore, applying them to another domain needs a new dataset because the neural models are generally trained to imitate the given dataset. In this paper, we propose Schema Encoding for Transferable Dialogue State Tracking (SETDST), which is a neural DST method for effective transfer to new domains. Transferable DST could assist developments of dialogue systems even with few dataset on target domains. We use a schema encoder not just to imitate the dataset but to comprehend the schema of the dataset. We aim to transfer the model to new domains by encoding new schemas and using them for DST on multi-domain settings. As a result, SET-DST improved the joint accuracy by 1.46 points on MultiWOZ 2.1.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 29 references that are in the map. First posted on arXiv 5 Oct 2022.

Builds on

21 of 29 references.

References · 21

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2019Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Datasetcited by 434
  4. 2020A Simple Language Model for Task-Oriented Dialoguecited by 332
  5. 2019Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systemscited by 216
  6. 2021Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue Systemcited by 139
  7. 2020TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Trackingcited by 130
  8. 2021KLUE: Korean Language Understanding Evaluationcited by 114
  9. 2020MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systemscited by 103
  10. 2020DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialoguecited by 89
  11. 2019Efficient Dialogue State Tracking by Selectively Overwriting Memorycited by 76
  12. 2019Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Trackingcited by 75
9 more
  1. 2019SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Trackingcited by 63
  2. 2020A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learningcited by 47
  3. 2018Global-Locally Self-Attentive Dialogue State Trackercited by 45
  4. 2020CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackerscited by 43
  5. 2019Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answeringcited by 39
  6. 2021Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialog State Trackingcited by 35
  7. 2018Towards Universal Dialogue State Trackingcited by 33
  8. 2020Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Trackingcited by 27
  9. 2018Toward Scalable Neural Dialogue State Tracking Modelcited by 11

Built on by

Not cited yet.