14 citations · 21 across the 7 of their papers we have counts for
13 papers
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…
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
Ekaterina Vylomova, Jennifer White, Elizabeth Salesky +25
A broad goal in natural language processing (NLP) is to develop a system that has the capacity to process any natural language. Most systems, however, are developed using data from…
The SIGMORPHON 2020 Shared Task on Unsupervised Morphological Paradigm Completion
Katharina Kann, Arya McCarthy, Garrett Nicolai +1
In this paper, we describe the findings of the SIGMORPHON 2020 shared task on unsupervised morphological paradigm completion (SIGMORPHON 2020 Task 2), a novel task in the field of…
Applying the Transformer to Character-level Transduction
Shijie Wu, Ryan Cotterell, Mans Hulden
The transformer has been shown to outperform recurrent neural network-based sequence-to-sequence models in various word-level NLP tasks. Yet for character-level transduction tasks,…