UniMorph 4.0: Universal Morphology
- URL: http://arxiv.org/abs/2205.03608v2
- Date: Tue, 10 May 2022 05:29:45 GMT
- Title: UniMorph 4.0: Universal Morphology
- Authors: Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash,
Witold Kiera\'s, G\'abor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman,
Yustinus Ghanggo Ate, Maria Ryskina, Sabrina J. Mielke, Elena Budianskaya,
Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Lane, Mohit Raj,
Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Esa\'u
Zumaeta Rojas, Didier L\'opez Francis, Arturo Oncevay, Juan L\'opez 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, Ritv\'an Karah\'o\v{g}a, 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
- Abstract summary: This paper presents the expansions and improvements made on several fronts over the last couple of years.
Collaborative efforts by numerous linguists have added 67 new languages, including 30 endangered languages.
In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages.
- Score: 104.69846084893298
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 language-independent 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.
Related papers
- A Morphology-Based Investigation of Positional Encodings [46.667985003225496]
Morphology and word order are closely linked, with the latter incorporated into transformer-based models through positional encodings.
This prompts a fundamental inquiry: Is there a correlation between the morphological complexity of a language and the utilization of positional encoding in pre-trained language models?
In pursuit of an answer, we present the first study addressing this question, encompassing 22 languages and 5 downstream tasks.
arXiv Detail & Related papers (2024-04-06T07:10:47Z) - MYTE: Morphology-Driven Byte Encoding for Better and Fairer Multilingual Language Modeling [70.34758460372629]
We introduce a new paradigm that encodes the same information with segments of consistent size across diverse languages.
MYTE produces shorter encodings for all 99 analyzed languages.
This, in turn, improves multilingual LM performance and diminishes the perplexity gap throughout diverse languages.
arXiv Detail & Related papers (2024-03-15T21:21:11Z) - Morphosyntactic probing of multilingual BERT models [41.83131308999425]
We introduce an extensive dataset for multilingual probing of morphological information in language models.
We find that pre-trained Transformer models (mBERT and XLM-RoBERTa) learn features that attain strong performance across these tasks.
arXiv Detail & Related papers (2023-06-09T19:15:20Z) - Modeling Target-Side Morphology in Neural Machine Translation: A
Comparison of Strategies [72.56158036639707]
Morphologically rich languages pose difficulties to machine translation.
A large amount of differently inflected word surface forms entails a larger vocabulary.
Some inflected forms of infrequent terms typically do not appear in the training corpus.
Linguistic agreement requires the system to correctly match the grammatical categories between inflected word forms in the output sentence.
arXiv Detail & Related papers (2022-03-25T10:13:20Z) - Morphological Reinflection with Multiple Arguments: An Extended
Annotation schema and a Georgian Case Study [7.245355976804435]
We extend the UniMorph morphological dataset to cover verbs that agree with multiple arguments using true affixes.
The dataset has 4 times more tables and 6 times more verb forms compared to the existing UniMorph dataset.
It is expected to improve the coverage, consistency and interpretability of this benchmark.
arXiv Detail & Related papers (2022-03-16T10:47:29Z) - Morphology Without Borders: Clause-Level Morphological Annotation [8.559428282730021]
We propose to view morphology as a clause-level phenomenon, rather than word-level.
We deliver a novel dataset for clause-level morphology covering 4 typologically-different languages: English, German, Turkish and Hebrew.
Our experiments show that the clause-level tasks are substantially harder than the respective word-level tasks, while having comparable complexity across languages.
arXiv Detail & Related papers (2022-02-25T17:20:28Z) - Morphology Matters: A Multilingual Language Modeling Analysis [8.791030561752384]
Prior studies disagree on whether inflectional morphology makes languages harder to model.
We compile a larger corpus of 145 Bible translations in 92 languages and a larger number of typological features.
Several morphological measures are significantly associated with higher surprisal when LSTM models are trained with BPE-segmented data.
arXiv Detail & Related papers (2020-12-11T11:55:55Z) - XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning [68.57658225995966]
Cross-lingual Choice of Plausible Alternatives (XCOPA) is a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages.
We evaluate a range of state-of-the-art models on this novel dataset, revealing that the performance of current methods falls short compared to translation-based transfer.
arXiv Detail & Related papers (2020-05-01T12:22:33Z) - Universal Dependencies v2: An Evergrowing Multilingual Treebank
Collection [33.86322085911299]
Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages.
We describe version 2 of the guidelines (UD v2), discuss the major changes from UD v1 to UD v2, and give an overview of the currently available treebanks for 90 languages.
arXiv Detail & Related papers (2020-04-22T15:38:18Z) - A Simple Joint Model for Improved Contextual Neural Lemmatization [60.802451210656805]
We present a simple joint neural model for lemmatization and morphological tagging that achieves state-of-the-art results on 20 languages.
Our paper describes the model in addition to training and decoding procedures.
arXiv Detail & Related papers (2019-04-04T02:03:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.