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A Result based Portable Framework for Spoken Language Understanding

arXiv 2103.06010 ↗First posted 10 Mar 2021cs.CLcited by 4

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Abstract (arXiv)

Spoken language understanding (SLU), which is a core component of the task-oriented dialogue system, has made substantial progress in the research of single-turn dialogue. However, the performance in multi-turn dialogue is still not satisfactory in the sense that the existing multi-turn SLU methods have low portability and compatibility for other single-turn SLU models. Further, existing multi-turn SLU methods do not exploit the historical predicted results when predicting the current utterance, which wastes helpful information. To gap those shortcomings, in this paper, we propose a novel Result-based Portable Framework for SLU (RPFSLU). RPFSLU allows most existing single-turn SLU models to obtain the contextual information from multi-turn dialogues and takes full advantage of predicted results in the dialogue history during the current prediction. Experimental results on the public dataset KVRET have shown that all SLU models in baselines acquire enhancement by RPFSLU on multi-turn SLU tasks.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 18 of its 25 references that are in the map. First posted on arXiv 10 Mar 2021.

Builds on

18 of 25 references.

References · 18

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019BERT for Joint Intent Classification and Slot Fillingcited by 195
  3. 2016Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Fillingcited by 137
  4. 2017Key-Value Retrieval Networks for Task-Oriented Dialoguecited by 137
  5. 2019A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understandingcited by 106
  6. 2016Learning End-to-End Goal-Oriented Dialogcited by 104
  7. 2019A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Fillingcited by 85
  8. 2018Joint Slot Filling and Intent Detection via Capsule Neural Networkscited by 73
  9. 2020A Co-Interactive Transformer for Joint Slot Filling and Intent Detectioncited by 61
  10. 2018A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Fillingcited by 53
  11. 2021A Survey on Spoken Language Understanding: Recent Advances and New Frontierscited by 53
  12. 2019Legal Judgment Prediction via Multi-Perspective Bi-Feedback Networkcited by 50
6 more
  1. 2019CM-Net: A Novel Collaborative Memory Network for Spoken Language Understandingcited by 34
  2. 2020DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classificationcited by 28
  3. 2016Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Fillingcited by 25
  4. 2017Sequential Dialogue Context Modeling for Spoken Language Understandingcited by 11
  5. 2019Multi-Task Networks With Universe, Group, and Task Feature Learningcited by 11
  6. 2019Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inferencecited by 3

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Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 1.

4 papers.

  1. 2021An Effective Non-Autoregressive Model for Spoken Language Understandingcited by 5
  2. 2022A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understandingcited by 3
  3. 2022Capture Salient Historical Information: A Fast and Accurate Non-Autoregressive Model for Multi-turn Spoken Language Understandingcited by 2
  4. 2024Towards Spoken Language Understanding via Multi-level Multi-grained Contrastive Learningcited by 0