Tamil is a relatively low-resource language in the field of Natural Language Processing (NLP). Recent years have seen a growth in Tamil NLP datasets in Natural Language Understanding (NLU) or Natural Language Generation (NLG) tasks, but high-quality linguistic resources remain scarce. In order to alleviate this gap in resources, this paper introduces Aalamaram, a treebank with rich linguistic annotations for the Tamil language. It is hitherto the largest publicly available Tamil treebank with almost 10,000 sentences from diverse sources and is annotated for the tasks of Part-of-speech (POS) tagging, Named Entity Recognition (NER), Morphological Parsing and Dependency Parsing. Close attention has also been paid to multi-word segmentation, especially in the context of Tamil clitics. Although the treebank is based largely on the Universal Dependencies (UD) specifications, significant effort has been made to adjust the annotation rules according to the idiosyncrasies and complexities of the Tamil language, thereby providing a valuable resource for linguistic research and NLP developments.

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.