RNN for Spoken Language Understanding
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Updated
Jul 12, 2017 - Python
RNN for Spoken Language Understanding
Learning a common representation space from speech and text for cross-modal retrieval given textual queries and speech files.
Real-time Spoken Language Understanding for Orthopedic Training in Virtual Reality
Spoken NER implementation based on Wav2Vec2-XLS-R with experiments on transfer learning
Official repository of the SUGAR Task at Evalita 2018
Data Augmentation with Atomic Templates for Spoken Language Understanding
The open source codes for ICONIP 2023 Paper “A deep joint model of Multi-Scale intent-slots Interaction with Second-Order Gate for SLU”.
Memory consolidation for Contextual SLU with Multi-task Framework
cross-domain slot filling task with BERT
Code for the paper "Textual supervision for visually grounded spoken language understanding".
Library for training visually-grounded models of spoken language understanding.
🦁 BERT for Spoken Language Understanding in Task-based Dialog
"An Investigation of the Combination of Rehearsal and Knowledge Distillation in Continual Learning for Spoken Language Understanding", accepted at INTERSPEECH 2023.
This repository is a comprehensive project that leverages the XLM-Roberta model for intent detection. This repository is a valuable resource for developers looking to build and fine-tune intent detection models based on state-of-the-art techniques.
Source code and data for the journal ``Dual learning for semi-supervised natural language understanding" in TASLP 2020.
A TensorFlow implement for "A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding".
Source code for ASRU 2019 paper "Adapting Pretrained Transformer to Lattices for Spoken Language Understanding"
Semi-supervised spoken language understanding (SLU) via self-supervised speech and language model pretraining
Spoken Language Understanding(SLU)/Slot Filling(语义槽填充) in PyTorch
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