mlsanityData to 14 Oct 2026

OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding

arXiv 2305.10231 ↗First posted 17 May 2023cs.CLcited by 2

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Abstract (arXiv)

Spoken Language Understanding (SLU) is one of the core components of a task-oriented dialogue system, which aims to extract the semantic meaning of user queries (e.g., intents and slots). In this work, we introduce OpenSLU, an open-source toolkit to provide a unified, modularized, and extensible toolkit for spoken language understanding. Specifically, OpenSLU unifies 10 SLU models for both single-intent and multi-intent scenarios, which support both non-pretrained and pretrained models simultaneously. Additionally, OpenSLU is highly modularized and extensible by decomposing the model architecture, inference, and learning process into reusable modules, which allows researchers to quickly set up SLU experiments with highly flexible configurations. OpenSLU is implemented based on PyTorch, and released at urlhttps://github.com/LightChen233/OpenSLU.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 22 of its 36 references that are in the map. First posted on arXiv 17 May 2023.

Builds on

22 of 36 references.

References · 22

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
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  4. 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,254
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  6. 2018Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfacescited by 377
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  9. 2019A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understandingcited by 107
  10. 2019A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Fillingcited by 85
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  12. 2020A Co-Interactive Transformer for Joint Slot Filling and Intent Detectioncited by 61
10 more
  1. 2018A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Fillingcited by 53
  2. 2021A Survey on Spoken Language Understanding: Recent Advances and New Frontierscited by 53
  3. 2019Attention-Informed Mixed-Language Training for Zero-shot Cross-lingual Task-oriented Dialogue Systemscited by 42
  4. 2019CM-Net: A Novel Collaborative Memory Network for Spoken Language Understandingcited by 34
  5. 2021GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Fillingcited by 33
  6. 2022MULTI3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialoguecited by 15
  7. 2022Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphscited by 14
  8. 2021Text is no more Enough! A Benchmark for Profile-based Spoken Language Understandingcited by 7
  9. 2022A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understandingcited by 3
  10. 2023HIT-SCIR at MMNLU-22: Consistency Regularization for Multilingual Spoken Language Understandingcited by 2

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Citing papers per year: 2024: 2.

2 papers.

  1. 2024Modeling Output-Level Task Relatedness in Multi-Task Learning with Feedback Mechanismcited by 0
  2. 2024Towards Spoken Language Understanding via Multi-level Multi-grained Contrastive Learningcited by 0