| Challenge: | Publicly available datasets for Spoken Language Understanding (SLU) are limited. |
| Approach: | They propose a publicly available SLU resource package that includes a multi-domain dataset in English spanning 18 domains. |
| Outcome: | The proposed dataset is bigger and more diverse than existing datasets. |
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SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks (2023.acl-long)
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Suwon Shon, Siddhant Arora, Chyi-Jiunn Lin, Ankita Pasad, Felix Wu, Roshan S Sharma, Wei-Lun Wu, Hung-yi Lee, Karen Livescu, Shinji Watanabe
| Challenge: | Spoken language understanding (SLU) tasks have received little attention and resources compared to lower-level tasks like speech and speaker recognition. |
| Approach: | They propose annotated SLU benchmark tasks based on freely available speech data to complement existing benchmarks and address gaps in the evaluation landscape. |
| Outcome: | The proposed benchmarks complement existing benchmarks and address gaps in the evaluation landscape. |
The Spoken Language Understanding MEDIA Benchmark Dataset in the Era of Deep Learning: data updates, training and evaluation tools (2022.lrec-1)
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Gaëlle Laperrière, Valentin Pelloin, Antoine Caubrière, Salima Mdhaffar, Nathalie Camelin, Sahar Ghannay, Bassam Jabaian, Yannick Estève
| Challenge: | a growing number of studies address the spoken language understanding domain through a simple task like speech intent detection. |
| Approach: | They focus on the french MEDIA SLU dataset, which is distributed since 2005 . they propose a recipe for its use, including data preparation, training and evaluation scripts . |
| Outcome: | The MEDIA SLU dataset is used as a benchmark dataset for a large number of research projects. |
To What Degree Can Language Borders Be Blurred In BERT-based Multilingual Spoken Language Understanding? (2020.coling-main)
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| Challenge: | Existing models for multilingual SLU are mostly DNN-based joint models of intent classification and slot filling. |
| Approach: | They propose a BERT-based adversarial model architecture to learn language-shared and language-specific representations for multilingual SLU. |
| Outcome: | The proposed model narrows the gap to the ideal multilingual performance. |
On the Evaluation of Speech Foundation Models for Spoken Language Understanding (2024.findings-acl)
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Siddhant Arora, Ankita Pasad, Chung-Ming Chien, Jionghao Han, Roshan Sharma, Jee-weon Jung, Hira Dhamyal, William Chen, Suwon Shon, Hung-yi Lee, Karen Livescu, Shinji Watanabe
| Challenge: | Spoken language understanding evaluation (SLUE) benchmarks are used to benchmark complex spoken language understanding tasks on natural speech. |
| Approach: | They propose a set of benchmark tasks to evaluate spoken language understanding on natural speech . they use pre-trained speech foundation models to evaluate the utility of different SFMs . |
| Outcome: | The proposed framework outperforms pre-trained speech foundation models on natural speech . the proposed framework also outperformed self-supervised SFMs on the sequence generation tasks . |
The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation (2023.findings-emnlp)
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| Challenge: | Modern artificial intelligence is characterized by large pretrained language models with strong language capabilities to be adapted to various downstream tasks. |
| Approach: | They propose to use the task of speech translation (ST) to pretrain speech models for end-to-end SLU on intra- and cross-lingual scenarios. |
| Outcome: | The proposed model achieves higher performance over baselines on monolingual and multilingual intent classification as well as spoken question answering using SLURP, MINDS-14, and NMSQA benchmarks. |
MoE-SLU: Towards ASR-Robust Spoken Language Understanding via Mixture-of-Experts (2024.findings-acl)
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| Challenge: | Spoken language understanding (SLU) is a crucial task in task-oriented dialogue systems. |
| Approach: | They propose an ASR-Robust SLU framework based on the mixture-of-experts technique to generate additional transcripts from clean transcripts and use it to weigh the representations of the generated transcripts, ASR transcripts . |
| Outcome: | The proposed framework achieves state-of-the-art on three benchmark SLU datasets. |
TARIC-SLU: A Tunisian Benchmark Dataset for Spoken Language Understanding (2024.lrec-main)
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| Challenge: | Existing SLU resources are limited in high-resource languages such as English, Mandarin and French. |
| Approach: | They propose to use a Tunisian dialect dataset to build a semantic model of the system that is continuously annotated with dialogue acts and slots. |
| Outcome: | The proposed dataset is based on train-based and ASR-based models of train-driven conversations in Tunisian dialect. |
SLTEV: Comprehensive Evaluation of Spoken Language Translation (2021.eacl-demos)
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| Challenge: | Spoken Language Translation (SLT) evaluation of machine translation (MT) quality has been investigated for decades. |
| Approach: | They propose an open-source tool for assessing machine translation (MT) quality based on time-stamped transcripts and reference translations. |
| Outcome: | The proposed evaluation tool is based on time-stamped transcripts and reference translations into a target language. |
skLEP: A Slovak General Language Understanding Benchmark (2025.findings-acl)
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Marek Suppa, Andrej Ridzik, Daniel Hládek, Tomáš Javůrek, Viktória Ondrejová, Kristína Sásiková, Martin Tamajka, Marian Simko
| Challenge: | skLEP is the first comprehensive benchmark specifically designed for evaluating Slovak natural language understanding models. |
| Approach: | They introduce a benchmark specifically designed for evaluating Slovak natural language understanding models. |
| Outcome: | The proposed benchmark covers nine tasks that span token-level, sentence-pair, document-level tasks. |
OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding (2023.acl-demo)
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| Challenge: | Spoken Language Understanding (SLU) is a task-oriented dialogue system . open-source toolkit provides a unified, modularized, and extensible toolkit for SLU . |
| Approach: | They introduce an open-source toolkit to provide a unified toolkit for spoken language understanding. |
| Outcome: | The proposed toolkit unifies 10 models for both single-intent and multi-intention scenarios. |