ITALIC: An Italian Intent Classification Dataset
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911930098122752 |
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| author | Koudounas, Alkis La Quatra, Moreno Vaiani, Lorenzo Colomba, Luca Attanasio, Giuseppe Pastor, Eliana Cagliero, Luca Baralis, Elena |
| author_facet | Koudounas, Alkis La Quatra, Moreno Vaiani, Lorenzo Colomba, Luca Attanasio, Giuseppe Pastor, Eliana Cagliero, Luca Baralis, Elena |
| contents | Recent large-scale Spoken Language Understanding datasets focus predominantly on English and do not account for language-specific phenomena such as particular phonemes or words in different lects. We introduce ITALIC, the first large-scale speech dataset designed for intent classification in Italian. The dataset comprises 16,521 crowdsourced audio samples recorded by 70 speakers from various Italian regions and annotated with intent labels and additional metadata. We explore the versatility of ITALIC by evaluating current state-of-the-art speech and text models. Results on intent classification suggest that increasing scale and running language adaptation yield better speech models, monolingual text models outscore multilingual ones, and that speech recognition on ITALIC is more challenging than on existing Italian benchmarks. We release both the dataset and the annotation scheme to streamline the development of new Italian SLU models and language-specific datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_08502 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ITALIC: An Italian Intent Classification Dataset Koudounas, Alkis La Quatra, Moreno Vaiani, Lorenzo Colomba, Luca Attanasio, Giuseppe Pastor, Eliana Cagliero, Luca Baralis, Elena Computation and Language Sound Audio and Speech Processing Recent large-scale Spoken Language Understanding datasets focus predominantly on English and do not account for language-specific phenomena such as particular phonemes or words in different lects. We introduce ITALIC, the first large-scale speech dataset designed for intent classification in Italian. The dataset comprises 16,521 crowdsourced audio samples recorded by 70 speakers from various Italian regions and annotated with intent labels and additional metadata. We explore the versatility of ITALIC by evaluating current state-of-the-art speech and text models. Results on intent classification suggest that increasing scale and running language adaptation yield better speech models, monolingual text models outscore multilingual ones, and that speech recognition on ITALIC is more challenging than on existing Italian benchmarks. We release both the dataset and the annotation scheme to streamline the development of new Italian SLU models and language-specific datasets. |
| title | ITALIC: An Italian Intent Classification Dataset |
| topic | Computation and Language Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2306.08502 |