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Ontology-Enhanced Slot Filling

arXiv 2108.11275 ↗First posted 25 Aug 2021cs.CLcited by 0

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Abstract (arXiv)

Slot filling is a fundamental task in dialog state tracking in task-oriented dialog systems. In multi-domain task-oriented dialog system, user utterances and system responses may mention multiple named entities and attributes values. A system needs to select those that are confirmed by the user and fill them into destined slots. One difficulty is that since a dialogue session contains multiple system-user turns, feeding in all the tokens into a deep model such as BERT can be challenging due to limited capacity of input word tokens and GPU memory. In this paper, we investigate an ontology-enhanced approach by matching the named entities occurred in all dialogue turns using ontology. The matched entities in the previous dialogue turns will be accumulated and encoded as additional inputs to a BERT-based dialogue state tracker. In addition, our improvement includes ontology constraint checking and the correction of slot name tokenization. Experimental results showed that our ontology-enhanced dialogue state tracker improves the joint goal accuracy (slot F1) from 52.63% (91.64%) to 53.91% (92%) on MultiWOZ 2.1 corpus.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 21 references that are in the map. First posted on arXiv 25 Aug 2021.

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References · 17

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  3. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
  4. 2018MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modellingcited by 760
  5. 2016Incorporating Copying Mechanism in Sequence-to-Sequence Learningcited by 217
  6. 2016Neural Belief Tracker: Data-Driven Dialogue State Trackingcited by 142
  7. 2020TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Trackingcited by 130
  8. 2019Efficient Dialogue State Tracking by Selectively Overwriting Memorycited by 76
  9. 2019Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Trackingcited by 75
  10. 2019SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Trackingcited by 63
  11. 2019Dialog State Tracking: A Neural Reading Comprehension Approachcited by 62
  12. 2018Global-Locally Self-Attentive Dialogue State Trackercited by 45
5 more
  1. 2019BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformercited by 37
  2. 2018An End-to-end Approach for Handling Unknown Slot Values in Dialogue State Trackingcited by 27
  3. 2020Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Trackingcited by 27
  4. 2020MA-DST: Multi-Attention Based Scalable Dialog State Trackingcited by 24
  5. 2017An End-to-End Trainable Neural Network Model with Belief Tracking for Task-Oriented Dialogcited by 17

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