Challenge: morphological annotations are a common problem in some languages, but the flat structure of the current schema makes it impossible to treat them.
Approach: They propose a general solution for polypersonal agreement in Georgian language . they extend the existing UniMorph annotation schema to address this problem .
Outcome: The proposed framework covers all possible variants of argument marking, and is accurate and balanced.

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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.
Wikinflection Corpus: A (Better) Multilingual, Morpheme-Annotated Inflectional Corpus (2020.lrec-1)

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Challenge: Inflectional corpora with annotated morpheme boundaries are scarce in the NLP community . a generated, multilingual inflectional lexicon with morphological features is not as good as UniMorph's .
Approach: They evaluate a multilingual inflectional corpus with morpheme boundaries from the English Wiktionary and the UniMorph project's inflection corpus.
Outcome: The generated Wikinflection corpus is not as good as UniMorph's, but extracts significant amount of words from the intersection of the two corpora.
UniMorph 3.0: Universal Morphology (2020.lrec-1)

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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.
Morphology Without Borders: Clause-Level Morphology (2022.tacl-1)

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Challenge: Morphological tasks use large multi-lingual datasets that organize words into inflection tables . lack of a clear linguistic and operational definition of what is a word impairs universality of tasks .
Approach: They propose to view morphology as a clause-level phenomenon, rather than word-level . they propose to use a dataset for clause- level morphological tasks in 4 different languages .
Outcome: The proposed dataset for clause-level morphology covers 4 typologically different languages: English, German, Turkish, and Hebrew.
UniMorph 2.0: Universal Morphology (L18-1)

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Challenge: The Universal Morphology project is a collaborative effort to improve how NLP handles complex morphology across the world's languages.
Approach: They propose to use a universal tagset to annotate morphological data using a schema that includes a lemma and a bundle of morphology features.
Outcome: The project releases annotated morphological data using a universal tagset, the UniMorph schema.
K-UniMorph: Korean Universal Morphology and its Feature Schema (2023.findings-acl)

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Challenge: Previously, the Korean language has been underrepresented in the field of morphological paradigms amongst hundreds of diverse world languages.
Approach: They propose a new Universal Morphology dataset for Korean that preserves its distinct characteristics.
Outcome: The proposed dataset extracts inflected Korean verb forms from the largest annotated corpus for Korean.
(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models’ Performance (2022.acl-short)

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Challenge: Inflection tasks have gained a lot of traction in recent years, mostly via SIGMORPHON's shared-tasks.
Approach: They propose to use split-by-lemma to challenge the generalization capacity of morphological inflection models by employing harder train-test splits.
Outcome: The proposed method is based on a split-by-lemma method that challenges the generalization capacity of the models.
Exploring Linguistic Probes for Morphological Inflection (2023.emnlp-main)

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Challenge: morphological inflection models typically employ language-independent data splitting algorithms.
Approach: They propose language-specific probes to test aspects of morphological generalization . they use three morphology-distinct languages to test their generalization abilities .
Outcome: The proposed language-specific probes are used to test morphological generalization abilities on three distinct languages.
Joint Annotation of Morphology and Syntax in Dependency Treebanks (2024.lrec-main)

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Challenge: Syntactic treebanks have been in development since the 1970s . they are now available for a vast array of languages from across the globe .
Approach: They propose new formats to annotate syntactic and morphological relations in a dependency treebank using distributional criteria for the choice of the head of any combination.
Outcome: The proposed formats are compatible with the UD schema for syntactic treebanks.
A Multi-layer Annotated Corpus of Argumentative Text: From Argument Schemes to Discourse Relations (L18-1)

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Challenge: Recent interest in Argumentation Mining has brought to the fore the need for corpora annotated with argument information, which can be used as training data.
Approach: They propose a set of guidelines for the annotation of argument schemes and a new annotation tool for the 'inferential' argument schemes.
Outcome: The proposed corpus includes 112 argumentative microtexts and a new annotation tool.

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