IndoToD: A Multi-Domain Indonesian Benchmark For End-to-End Task-Oriented Dialogue Systems
This addresses the need for task-oriented dialogue systems in regional languages like Indonesian, providing a benchmark for evaluation and cross-lingual transfer learning, though it is incremental as it adapts existing datasets.
The paper introduces IndoToD, a multi-domain Indonesian benchmark for end-to-end task-oriented dialogue systems, by extending two English datasets to Indonesian through delexicalization and manual translation, resulting in a dataset covering four domains.
Task-oriented dialogue (ToD) systems have been mostly created for high-resource languages, such as English and Chinese. However, there is a need to develop ToD systems for other regional or local languages to broaden their ability to comprehend the dialogue contexts in various languages. This paper introduces IndoToD, an end-to-end multi domain ToD benchmark in Indonesian. We extend two English ToD datasets to Indonesian, comprising four different domains by delexicalization to efficiently reduce the size of annotations. To ensure a high-quality data collection, we hire native speakers to manually translate the dialogues. Along with the original English datasets, these new Indonesian datasets serve as an effective benchmark for evaluating Indonesian and English ToD systems as well as exploring the potential benefits of cross-lingual and bilingual transfer learning approaches.