Developing dialogue summarization for extremely low-resource languages is a challenging task. We introduce NusaDialogue, a dialogue summarization dataset for three underrepresented languages in the Malayo-Polynesian language family: Minangkabau, Balinese, and Buginese. NusaDialogue covers 17 topics and 185 subtopics, with annotations provided by 73 native speakers. Additionally, we conducted experiments using fine-tuning on a specifically designed medium-sized language model for Indonesian, as well as zero- and few-shot learning on various multilingual large language models (LLMs). The results indicate that, for extremely low-resource languages such as Minangkabau, Balinese, and Buginese, the fine-tuning approach yields significantly higher performance compared to zero- and few-shot prompting, even when applied to LLMs with considerably larger parameter sizes.

Large Language Models (LLMs) are increasingly being used to generate synthetic data for training and evaluating models. However, it is unclear whether they can generate a good quality of question answering (QA) dataset that incorporates knowledge and cultural nuance embedded in a language, especially for low-resource languages. In this study, we investigate the effectiveness of using LLMs in generating culturally relevant commonsense QA datasets for Indonesian and Sundanese languages. To do so, we create datasets for these languages using various methods involving both LLMs and human annotators, resulting in 4.5K questions per language (9K in total), making our dataset the largest of its kind. Our experiments show that automatic data adaptation from an existing English dataset is less effective for Sundanese. Interestingly, using the direct generation method on the target language, GPT-4 Turbo can generate questions with adequate general knowledge in both languages, albeit not as culturally ‘deep’ as humans. We also observe a higher occurrence of fluency errors in the Sundanese dataset, highlighting the discrepancy between medium- and lower-resource languages.

Constituency parsing is an important task of informing how words are combined to form sentences. While constituency parsing in English has seen significant progress in the last few years, tools for constituency parsing in Indonesian remain few and far between. In this work, we publish ICON (Indonesian CONstituency treebank), the hitherto largest publicly-available manually-annotated benchmark constituency treebank for the Indonesian language with a size of 10,000 sentences and approximately 124,000 constituents and 182,000 tokens, which can support the training of state-of-the-art transformer-based models. We establish strong baselines on the ICON dataset using the Berkeley Neural Parser with transformer-based pre-trained embeddings, with the best performance of 88.85% F1 score coming from our own version of SpanBERT (IndoSpanBERT). We further analyze the predictions made by our best-performing model to reveal certain idiosyncrasies in the Indonesian language that pose challenges for constituency parsing.