14 citations · 22 across the 10 of their papers we have counts for
16 papers · 1 filter
Neural Induction of Finite-State Transducers
Michael Ginn, Alexis Palmer, Mans Hulden
Finite-State Transducers (FSTs) are effective models for string-to-string rewriting tasks, often providing the efficiency necessary for high-performance applications, but construct…
Historia Magistra Vitae: Dynamic Topic Modeling of Roman Literature using Neural Embeddings
Michael Ginn, Mans Hulden
Dynamic topic models have been proposed as a tool for historical analysis, but traditional approaches have had limited usefulness, being difficult to configure, interpret, and eval…
Can we teach language models to gloss endangered languages?
Michael Ginn, Mans Hulden, Alexis Palmer
Interlinear glossed text (IGT) is a popular format in language documentation projects, where each morpheme is labeled with a descriptive annotation. Automating the creation of inte…
Eeny, meeny, miny, moe. How to choose data for morphological inflection
Saliha Muradoglu, Mans Hulden
Data scarcity is a widespread problem in numerous natural language processing (NLP) tasks for low-resource languages. Within morphology, the labour-intensive work of tagging/glossi…
Can a Transformer Pass the Wug Test? Tuning Copying Bias in Neural Morphological Inflection Models
Ling Liu, Mans Hulden
Deep learning sequence models have been successfully applied to the task of morphological inflection. The results of the SIGMORPHON shared tasks in the past several years indicate…
Do RNN States Encode Abstract Phonological Processes?
Miikka Silfverberg, Francis Tyers, Garrett Nicolai +1
Sequence-to-sequence models have delivered impressive results in word formation tasks such as morphological inflection, often learning to model subtle morphophonological details wi…