Papers with UniMorph
Resisting the Lure of the Skyline: Grounding Practices in Active Learning for Morphological Inflection (2024.acl-short)
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| Challenge: | Several approaches to active learning are available, including confidence-based, diversity-based and committee-based. |
| Approach: | They propose to use a baseline and a skyline to measure the accuracy of the unannotated sample pool. |
| Outcome: | The proposed model outperforms a random selection baseline and a skyline approach. |
MultiBLiMP 1.0: A Massively Multilingual Benchmark of Linguistic Minimal Pairs (2026.tacl-1)
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| Challenge: | MultiBLiMP 1.0 is a massively multilingual benchmark of linguistic minimal pairs covering 101 languages and 2 types of subject-verb agreement. |
| Approach: | They propose to use multilingual benchmarks to evaluate linguistic minimal pairs in 101 languages and 2 types of subject-verb agreement to create the minimal pairs. |
| Outcome: | The proposed benchmark covers 101 languages and 2 types of subject-verb agreement, and contains more than 128,000 minimal pairs. |
UniMorph 2.0: Universal Morphology (L18-1)
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Christo Kirov, Ryan Cotterell, John Sylak-Glassman, Géraldine Walther, Ekaterina Vylomova, Patrick Xia, Manaal Faruqui, Sabrina J. Mielke, Arya McCarthy, Sandra Kübler, David Yarowsky, Jason Eisner, Mans Hulden
| 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. |
CaMEL: Case Marker Extraction without Labels (2022.acl-long)
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| Challenge: | Existing models for morphological case marking and semantic content are not isomorphic. |
| Approach: | They propose a model that extracts case markers from a multilingual corpus using a noun phrase chunker and an alignment system. |
| Outcome: | The proposed model can extract case markers in 83 languages and visualise similarities and differences between case systems and annotate fine-grained deep cases in languages where they are not overtly marked. |
Universal Morphologies for the Caucasus region (L18-1)
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Christian Chiarcos, Kathrin Donandt, Maxim Ionov, Monika Rind-Pawlowski, Hasmik Sargsian, Jesse Wichers Schreur, Frank Abromeit, Christian Fäth
| Challenge: | Caucasus region is famed for its rich and diverse arrays of languages and language families . authors describe efforts to improve the coverage of Universal Morphologies for languages of the region . |
| Approach: | They propose to improve the coverage of Universal Morphologies for Caucasus languages . they propose to complement the Universal Dependencies which focus on morphosyntax and syntax. |
| Outcome: | The proposed framework improves the coverage of languages of the Caucasus region . the proposed framework criticizes the UniMorph TSV format for its limited expressiveness . |
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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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. |
Annotation Interoperability for the Post-ISOCat Era (2020.lrec-1)
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| Challenge: | Using ISOCat successor solutions, annotation standards have been developed since 2010 . |
| Approach: | They describe ISOCat successor solutions and annotation standardization efforts since 2010 . they describe low-cost harmonization of post-ISOCat vocabularies by means of linked ontologies . |
| Outcome: | The proposed ontologies are linked with the Ontologie of Linguistic Annotation and ISOCat, the GOLD ontology, the Typological Database Systems ontological and a large number of annotation schemes. |