activity
20182022
most citedThe SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection

14 citations · 17 across the 6 of their papers we have counts for

collaborators

10 papers

cs.CL2022

Dim Wihl Gat Tun: The Case for Linguistic Expertise in NLP for Underdocumented Languages

Clarissa Forbes, Farhan Samir, Bruce Harold Oliver +4

Recent progress in NLP is driven by pretrained models leveraging massive datasets and has predominantly benefited the world's political and economic superpowers. Technologically un…

cs.CL20221 cited

Morphological Processing of Low-Resource Languages: Where We Are and What's Next

Adam Wiemerslage, Miikka Silfverberg, Changbing Yang +4

Automatic morphological processing can aid downstream natural language processing applications, especially for low-resource languages, and assist language documentation efforts for…

cs.CL20212 cited

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…

cs.CL2020

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…

cs.CL2020

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…

cs.CL2020

Cross-Linguistic Syntactic Evaluation of Word Prediction Models

Aaron Mueller, Garrett Nicolai, Panayiota Petrou-Zeniou +2

A range of studies have concluded that neural word prediction models can distinguish grammatical from ungrammatical sentences with high accuracy. However, these studies are based p…