Papers by Garrett Nicolai
The Johns Hopkins University Bible Corpus: 1600+ Tongues for Typological Exploration (2020.lrec-1)
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Arya D. McCarthy, Rachel Wicks, Dylan Lewis, Aaron Mueller, Winston Wu, Oliver Adams, Garrett Nicolai, Matt Post, David Yarowsky
| Challenge: | Our corpus spans 1611 diverse written languages, with constituents of more than 90 language families. |
| Approach: | They propose to scrape and merge online resources and merge them with existing corpora to create a verse-parallel scheme for all translations. |
| Outcome: | The results show that the Bible provides high coverage of core vocabulary. |
Multiple Sources are Better Than One: Incorporating External Knowledge in Low-Resource Glossing (2024.emnlp-main)
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| Challenge: | a paper addresses the data scarcity problem in automated glossing for low-resource languages . traditional manual documenting is laborintensive and a lack of data is limiting the accuracy of glossing . |
| Approach: | They propose to integrate token-level and sentence-level translations into models and integrate available dictionary resources into the model. |
| Outcome: | The proposed model improves word-level accuracy by 5% on the lowest-resource language Gitksan . the authors also show that the model improve on a simulated low-resourced language with fewer than 100 glossed sentences . |
Penalizing Divergence: Multi-Parallel Translation for Low-Resource Languages of North America (2022.coling-1)
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| Challenge: | Existing studies show that multi-parallel translation models can overfit when training data are limited. |
| Approach: | They introduce a regularizer which penalizes translation models when they represent source sentences with identical target translations in divergent ways. |
| Outcome: | The proposed model improves when the target data for all language pairs are identical. |
Multilingual Dictionary Based Construction of Core Vocabulary (2020.lrec-1)
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| Challenge: | Existing methods for core vocabulary lists for multiple applications are lacking coverage in sparse dictionaries . we propose a new method for definition and construction of core vocabulary sets based on coverage in dictionary dictionaria . |
| Approach: | They propose a functional definition and construction method for core vocabulary sets based on relative coverage of a target concept in bilingual dictionaries. |
| Outcome: | The proposed method achieves high overlap with existing vocabulary lists . it uses a cognate prediction method to recover missing coverage of the vocabulary . |
An Investigation of Noise in Morphological Inflection (2023.findings-acl)
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| Challenge: | Neural morphological inflection systems can be used for languages with very little supervised data, but are often less likely to have clean, goldstandard data. |
| Approach: | They propose an error taxonomy and annotation pipeline for inflection training data and propose a character-level masked language modeling (CMLM) pretraining objective. |
| Outcome: | The proposed pipeline is based on error taxonomy and annotation pipelines for unsupervised morphological paradigm completion. |
Noise Isn’t Always Negative: Countering Exposure Bias in Sequence-to-Sequence Inflection Models (2020.coling-main)
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| Challenge: | Morphological inflection is a sequence-to-sequence task that sees great performance when data is plentiful, but performance falls off sharply in lower-data settings. |
| Approach: | They hypothesize that teacher forcing increases the likelihood that a model too closely models its training data. |
| Outcome: | Experiments show that teacher forcing can overfit models when they enter unknown territory. |
Do RNN States Encode Abstract Phonological Alternations? (2021.naacl-main)
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| Challenge: | Sequence-to-sequence models have been successful in word formation tasks, but the opacity of the models makes it difficult to determine whether complex generalizations are learned or whether there is some level of generalization across related sound changes. |
| Approach: | They propose to train character-based sequence-to-sequence models for inflection of Finnish nouns into the genitive case, an inflation type which is encoded in the hidden states of an LSTM encoderdecoder trained to perform word infference. |
| Outcome: | The proposed models encode 17 different consonant gradation processes in a handful of dimensions in the RNN. |
Dim Wihl Gat Tun: The Case for Linguistic Expertise in NLP for Under-Documented Languages (2022.findings-acl)
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Clarissa Forbes, Farhan Samir, Bruce Oliver, Changbing Yang, Edith Coates, Garrett Nicolai, Miikka Silfverberg
| Challenge: | Recent progress in NLP is driven by pretrained models leveraging massive datasets. |
| Approach: | They argue that IGT data can be leveraged provided target language expertise is available and that it can be used to create effective models. |
| Outcome: | The proposed model can be leveraged provided that target language expertise is available. |
An Inflectional Database for Gitksan (2022.lrec-1)
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Bruce Oliver, Clarissa Forbes, Changbing Yang, Farhan Samir, Edith Coates, Garrett Nicolai, Miikka Silfverberg
| Challenge: | In this paper, we build a database of partial inflection tables for Gitksan, a low-resource Indigenous language of Canada. |
| Approach: | They use Gitksan data in interlinear glossed format to build a database of partial inflection tables and enrich it with neural transformer reinflection models. |
| Outcome: | The proposed model improves the performance of the experimental data hallucination and back-translation techniques. |
Morphological Processing of Low-Resource Languages: Where We Are and What’s Next (2022.findings-acl)
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Adam Wiemerslage, Miikka Silfverberg, Changbing Yang, Arya McCarthy, Garrett Nicolai, Eliana Colunga, Katharina Kann
| Challenge: | Existing models for morphological processing are not suitable for low-resource languages, but they are still lacking in the field of computational morphology. |
| Approach: | They propose to bridge two unsupervised models to understand a language’s morphology from raw text alone and propose to use them to improve their models. |
| Outcome: | The proposed models perform reasonably, but there is room for improvement. |
An Analysis of Massively Multilingual Neural Machine Translation for Low-Resource Languages (2020.lrec-1)
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| Challenge: | In this study, we explore massively multilingual low-resource neural machine translation. |
| Approach: | They propose to use Bible translations to train models with up to 1,107 source languages and create multilingual corpora varying the number and relatedness of source languages. |
| Outcome: | The proposed approach is highly language-specific and can be tailored to the source language and its typology. |
UniMorph 4.0: Universal Morphology (2022.lrec-1)
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Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieraś, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Abbott Lane, Mohit Raj, Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Esaú Zumaeta Rojas, Didier López Francis, Arturo Oncevay, Juan López Bautista, Gema Celeste Silva Villegas, Lucas Torroba Hennigen, Adam Ek, David Guriel, Peter Dirix, Jean-Philippe Bernardy, Andrey Scherbakov, Aziyana Bayyr-ool, Antonios Anastasopoulos, Roberto Zariquiey, Karina Sheifer, Sofya Ganieva, Hilaria Cruz, Ritván Karahóǧa, Stella Markantonatou, George Pavlidis, Matvey Plugaryov, Elena Klyachko, Ali Salehi, Candy Angulo, Jatayu Baxi, Andrew Krizhanovsky, Natalia Krizhanovskaya, Elizabeth Salesky, Clara Vania, Sardana Ivanova, Jennifer White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan North Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo M. Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Prud’hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya D. McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova
| Challenge: | The Universal Morphology project provides broad-coverage instantiated morphological inflection tables for hundreds of diverse languages. |
| Approach: | They propose a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. |
| Outcome: | The proposed schema has added 66 new languages, including 24 endangered languages. |
Cross-Linguistic Syntactic Evaluation of Word Prediction Models (2020.acl-main)
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| Challenge: | A range of studies have concluded that neural word prediction models can distinguish grammatical from ungrammatically sentences with high accuracy. |
| Approach: | They propose to use CLAMS to evaluate LSTM and multilingual BERT models. |
| Outcome: | The proposed model can learn syntax on English, French, German, Hebrew and Russian, and LSTM language models on multilingual and multilingual models. |
Fine-grained Morphosyntactic Analysis and Generation Tools for More Than One Thousand Languages (2020.lrec-1)
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| Challenge: | Using morphosyntactic tools, we train and distribute tools for approximately one thousand languages. |
| Approach: | They train and distribute morphosyntactic tools for approximately one thousand languages. |
| Outcome: | The results show that the tools generalize well across rare and common forms alike. |
Learning Morphosyntactic Analyzers from the Bible via Iterative Annotation Projection across 26 Languages (P19-1)
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| Challenge: | Currently, computational tools for low-resource languages are limited by a lack of supervised training data. |
| Approach: | They propose to use English taggers and parsers to project morphological information onto translations of the Bible in 26 different test languages. |
| Outcome: | The proposed method reduces lemmatization and morphological analysis over a strong initial system. |
UniMorph 3.0: Universal Morphology (2020.lrec-1)
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Arya D. McCarthy, Christo Kirov, Matteo Grella, Amrit Nidhi, Patrick Xia, Kyle Gorman, Ekaterina Vylomova, Sabrina J. Mielke, Garrett Nicolai, Miikka Silfverberg, Timofey Arkhangelskiy, Nataly Krizhanovsky, Andrew Krizhanovsky, Elena Klyachko, Alexey Sorokin, John Mansfield, Valts Ernštreits, Yuval Pinter, Cassandra L. Jacobs, Ryan Cotterell, Mans Hulden, David Yarowsky
| Challenge: | Explicit modeling of morphology has demonstrable benefits for language modeling, speech recognition, word embedding and keyword search. |
| Approach: | They propose a language-independent feature schema for rich morphological annotation and a type-level resource for annotated data in diverse languages. |
| Outcome: | The proposed schema has been improved to make it more complete and correct, and adds 66 new languages and parts of speech for 12 languages. |