Building Dialogue Understanding Models for Low-resource Language Indonesian from Scratch
This work addresses the challenge of enabling dialogue systems for low-resource languages like Indonesian, which is incremental as it builds on existing cross-lingual transfer methods.
The paper tackles the problem of building dialogue understanding models for low-resource Indonesian by investigating data requirements and proposing a cross-lingual transfer framework, achieving reliable and cost-efficient performance on manually annotated Indonesian data.
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.