mlsanityData to 14 Oct 2026

Building Dialogue Understanding Models for Low-resource Language Indonesian from Scratch

arXiv 2410.18430 ↗First posted 24 Oct 2024cs.CLcited by 0

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Abstract (arXiv)

Making use of off-the-shelf resources of resource-rich languages to transfer knowledge for low-resource languages raises much attention recently. The requirements of enabling the model to reach the reliable performance lack well guided, such as the scale of required annotated data or the effective framework. To investigate the first question, we empirically investigate the cost-effectiveness of several methods to train the intent classification and slot-filling models for Indonesia (ID) from scratch by utilizing the English data. Confronting the second challenge, we propose a Bi-Confidence-Frequency Cross-Lingual transfer framework (BiCF), composed by ``BiCF Mixing'', ``Latent Space Refinement'' and ``Joint Decoder'', respectively, to tackle the obstacle of lacking low-resource language dialogue data. Extensive experiments demonstrate our framework performs reliably and cost-efficiently on different scales of manually annotated Indonesian data. We release a large-scale fine-labeled dialogue dataset (ID-WOZ) and ID-BERT of Indonesian for further research.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 36 of its 62 references that are in the map. First posted on arXiv 24 Oct 2024.

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References · 36

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24 more
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