The

Cotterell, Ryan; Kirov, Christo; Sylak-Glassman, John; Walther, Géraldine; Vylomova, Ekaterina; McCarthy, Arya D.; Kann, Katharina; Mielke, Sabrina J.; Nicolai, Garrett; Silfverberg, Miikka; Yarowsky, David; Eisner, Jason; Hulden, Mans

doi:10.18653/v1/k18-3001

Cited by 25 publications

(3 citation statements)

References 29 publications

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“…For example, the inflectional realization of SJQ Chatino verb forms entails a mapping of the pairing of the lemma lyu1 'fall' with the tag-set 1;SG;PROG to the word form nlyon32. Morphological inflection has been thoroughly studied in monolingual high resource settings, especially through the recent SIGMORPHON challenges (Cotterell et al, 2016;Cotterell et al, 2017;Cotterell et al, 2018), with the latest iteration focusing more on low-resource settings, utilizing cross-lingual transfer (McCarthy et al, 2019). We use the guidelines of the state-of-the-art approach of (Anastasopoulos and Neubig, 2019) that achieved the highest inflection accuracy in the latest SIGMORPHON 2019 morphological inflection shared task.…”

Section: Baseline Resultsmentioning

confidence: 99%

A Resource for Studying Chatino Verbal Morphology

Cruz,

Stump,

Anastasopoulos

2020

Preprint

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We present the first resource focusing on the verbal inflectional morphology of San Juan Quiahije Chatino, a tonal mesoamerican language spoken in Mexico. We provide a collection of complete inflection tables of 198 lemmata, with morphological tags based on the UniMorph schema. We also provide baseline results on three core NLP tasks: morphological analysis, lemmatization, and morphological inflection.

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Section: Baseline Resultsmentioning

confidence: 99%

A Resource for Studying Chatino Verbal Morphology

Cruz,

Stump,

Anastasopoulos

2020

Preprint

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“…Out of these, Telugu is the only one that does not have a large data set (inflected word forms). We create the smaller data sets from the high-resource data sets using the sampling method based on probability distributions mentioned in Cotterell et al (2018). During training for smaller data sets, we use augmentation from Cotterell et al (2016).…”

Section: Datamentioning

confidence: 99%

How low is too low? A monolingual take on lemmatisation in Indian languages

Kumar¹,

Kumar²,

Bhattacharyya³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Most prior work on ML based lemmatization has focused on high resource languages, where data sets (word forms) are readily available. For languages which have no linguistic work available, especially on morphology or in languages where the computational realization of linguistic rules is complex and cumbersome, machine learning based lemmatizers are the way to go. In this paper, we devote our attention to lemmatisation for low resource, morphologically rich scheduled Indian languages using neural methods. Here, low resource means only a small number of word forms are available. We perform tests to analyse the variance in monolingual models' performance on varying the corpus size and contextual morphological tag data for training. We show that monolingual approaches with data augmentation can give competitive accuracy even in the low resource setting, which augurs well for NLP in low resource setting.

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“…UniMorph 3.0 introduced many under-resourced languages derived from various linguistic sources. Prior to each release, all language datasets were included in part in the SIGMORPHON shared tasks on morphological reinflection (Cotterell et al, 2016;Cotterell et al, 2017;Cotterell et al, 2018;McCarthy et al, 2019). The current release includes languages of the 2020-2021 shared tasks (Vylomova et al, 2020;Pimentel et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

UniMorph 4.0: Universal Morphology

Batsuren¹,

Goldman²,

Khalifa³

et al. 2022

Preprint

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The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a languageindependent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements made on several fronts over the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 67 new languages, including 30 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g. missing gender and macron information. We have also amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet.

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The

Cited by 25 publications

References 29 publications

A Resource for Studying Chatino Verbal Morphology

A Resource for Studying Chatino Verbal Morphology

How low is too low? A monolingual take on lemmatisation in Indian languages

UniMorph 4.0: Universal Morphology

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