3 accepted papers
Active learning (AL) aims to lower the demand of annotation by selecting informative unannotated samples for the model building. In this paper, we explore the importance of conscious experimental design in the language documentation and description setting, particularly the distribution of the unann…
Neural sequence-to-sequence models have been very successful at tasks in phonology and morphology that seemingly require a capacity for intricate linguistic generalisations. In this paper, we perform a detailed breakdown of the power of such models to capture various phonological generalisations and…
Data scarcity is a widespread problem for numerous natural language processing (NLP) tasks within low-resource languages. Within morphology, the labour-intensive task of tagging/glossing data is a serious bottleneck for both NLP and fieldwork. Active learning (AL) aims to reduce the cost of data ann…