55 citations · 75 across the 12 of their papers we have counts for
15 papers
Learning Beyond Limits: Multitask Learning and Synthetic Data for Low-Resource Canonical Morpheme Segmentation
Changbing Yang, Garrett Nicolai
We introduce a transformer-based morpheme segmentation system that augments a low-resource training signal through multitask learning and LLM-generated synthetic data. Our framewor…
Multiple Sources are Better Than One: Incorporating External Knowledge in Low-Resource Glossing
Changbing Yang, Garrett Nicolai, Miikka Silfverberg
In this paper, we address the data scarcity problem in automatic data-driven glossing for low-resource languages by coordinating multiple sources of linguistic expertise. We supple…
Embedded Translations for Low-resource Automated Glossing
Changbing Yang, Garrett Nicolai, Miikka Silfverberg
We investigate automatic interlinear glossing in low-resource settings. We augment a hard-attentional neural model with embedded translation information extracted from interlinear…
Neural Machine Translation Data Generation and Augmentation using ChatGPT
Wayne Yang, Garrett Nicolai
Neural models have revolutionized the field of machine translation, but creating parallel corpora is expensive and time-consuming. We investigate an alternative to manual parallel…
An Investigation of Noise in Morphological Inflection
Adam Wiemerslage, Changbing Yang, Garrett Nicolai +2
With a growing focus on morphological inflection systems for languages where high-quality data is scarce, training data noise is a serious but so far largely ignored concern. We ai…
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…
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…
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…
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…
Induced Inflection-Set Keyword Search in Speech
Oliver Adams, Matthew Wiesner, Jan Trmal +2
We investigate the problem of searching for a lexeme-set in speech by searching for its inflectional variants. Experimental results indicate how lexeme-set search performance chang…
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…
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…
String Transduction with Target Language Models and Insertion Handling
Garrett Nicolai, Saeed Najafi, Grzegorz Kondrak
Many character-level tasks can be framed as sequence-to-sequence transduction, where the target is a word from a natural language. We show that leveraging target language models de…