98 citations · 241 across the 17 of their papers we have counts for
19 papers
Compiling Rewrite Rules to Finite-State Transducers with the Worsening Trick
Mans Hulden, Michael Ginn
Finite-state transducers (FSTs) are essential for modeling string rewriting in computational linguistics and natural language processing (NLP), particularly for phonological and mo…
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…
UniMorph 4.0: Universal Morphology
Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa +93
The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world…
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,…
The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection
Arya D. McCarthy, Ekaterina Vylomova, Shijie Wu +9
The SIGMORPHON 2019 shared task on cross-lingual transfer and contextual analysis in morphology examined transfer learning of inflection between 100 language pairs, as well as cont…
Marrying Universal Dependencies and Universal Morphology
Arya D. McCarthy, Miikka Silfverberg, Ryan Cotterell +2
The Universal Dependencies (UD) and Universal Morphology (UniMorph) projects each present schemata for annotating the morphosyntactic details of language. Each project also provide…
UniMorph 2.0: Universal Morphology
Christo Kirov, Ryan Cotterell, John Sylak-Glassman +10
The Universal Morphology UniMorph project is a collaborative effort to improve how NLP handles complex morphology across the world's languages. The project releases annotated morph…
The CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection
Ryan Cotterell, Christo Kirov, John Sylak-Glassman +10
The CoNLL--SIGMORPHON 2018 shared task on supervised learning of morphological generation featured data sets from 103 typologically diverse languages. Apart from extending the numb…
On the Complexity and Typology of Inflectional Morphological Systems
Ryan Cotterell, Christo Kirov, Mans Hulden +1
We quantify the linguistic complexity of different languages' morphological systems. We verify that there is an empirical trade-off between paradigm size and irregularity: a langua…
On the Diachronic Stability of Irregularity in Inflectional Morphology
Ryan Cotterell, Christo Kirov, Mans Hulden +1
Many languages' inflectional morphological systems are replete with irregulars, i.e., words that do not seem to follow standard inflectional rules. In this work, we quantitatively…
A Comparison of Feature-Based and Neural Scansion of Poetry
Manex Agirrezabal, Iñaki Alegria, Mans Hulden
Automatic analysis of poetic rhythm is a challenging task that involves linguistics, literature, and computer science. When the language to be analyzed is known, rule-based systems…
CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages
Ryan Cotterell, Christo Kirov, John Sylak-Glassman +8
The CoNLL-SIGMORPHON 2017 shared task on supervised morphological generation required systems to be trained and tested in each of 52 typologically diverse languages. In sub-task 1,…