Part-of-Speech Tagging on an Endangered Language: a Parallel Griko-Italian Resource (C18-1)
Copied to clipboard
Antonios Anastasopoulos, Marika Lekakou, Josep Quer, Eleni Zimianiti, Justin DeBenedetto, David Chiang
| Challenge: | a recent study examines POS tagging techniques on endangered languages . most natural language processing applications have been tested on only a handful of languages - a problem that is compounded by the lack of standard orthography. |
| Approach: | They evaluate POS tagging techniques on an endangered language, Griko . they use a semi-supervised method with cross-lingual transfer to achieve better accuracy . |
| Outcome: | The proposed method achieves 72.9% accuracy on a sample of 114 narratives in a language . the proposed method improves by 21 percentage points over previous methods . |
Similar Papers
Unsupervised Cross-Lingual Part-of-Speech Tagging for Truly Low-Resource Scenarios (2020.emnlp-main)
Copied to clipboard
| Challenge: | a limited set of translations into one or more high-resource languages are available for POS tagging . a bi-LSTM architecture that uses contextualized word embeddings improves performance . |
| Approach: | They propose an unsupervised cross-lingual transfer approach for part-of-speech tagging . they use the Bible as parallel data to learn POS taggers for target languages . |
| Outcome: | The proposed approach improves accuracy on 12 diverse languages . the Bible is used as a parallel corpus for the study . |
A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages (N19-1)
Copied to clipboard
| Challenge: | Unsupervised part of speech (POS) tagging is often framed as a clustering problem, but taggers need to ground their clusters as well. |
| Approach: | They propose an approach for low-resource unsupervised part of speech (POS) tagging that yields fully grounded output and requires no labeled training data. |
| Outcome: | The proposed method achieves reasonable performance across languages, including Sinhalese and Kinyarwanda, with no labeled training data. |
POS Tagging for the Endangered Dagur Language (2024.lrec-main)
Copied to clipboard
| Challenge: | a recent study has focused on the so-called "dominant" languages, but it has not been inclusive in terms of language equality. |
| Approach: | They propose to use POS tagging to automatically annotate Dagur, an endangered Mongolic language . they use a manually annotated corpus to test transfer of models from other languages . |
| Outcome: | The proposed method can be used to document and revitalize endangered languages . the proposed model can be trained on Buryat, the only Mongolic language included in the corpus . |
Part-of-speech Tagging for Extremely Low-resource Indian Languages (2024.findings-acl)
Copied to clipboard
| Challenge: | Modern natural language processing systems thrive when given access to large datasets, but a large fraction of the world’s languages are not privy to such benefits due to sparse documentation and inadequate digital representation. |
| Approach: | They propose a parallel part-of-speech evaluation dataset for Angika, Magahi, Bhojpuri and Hindi. |
| Outcome: | The proposed approach improves F1 scores by up to 8% on Angika, Magahi, Bhojpuri and Hindi while ignoring the tokenization challenge. |
Agettivu, Aggitivu o Aghjettivu? POS Tagging Corsican Dialects (2024.lrec-main)
Copied to clipboard
| Challenge: | a series of experiments towards POS tagging Corsican are presented . POS tags are used to tag a less-resourced language spoken in corsica . |
| Approach: | They present a series of experiments towards POS tagging Corsican . they first contribute to the first gold standard POS-tagged corpus for Corsica . |
| Outcome: | The proposed model is the first POS tagger for Corsican, reaching an accuracy of 93.38%. |
MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African languages (2023.acl-long)
Copied to clipboard
Cheikh M. Bamba Dione, David Ifeoluwa Adelani, Peter Nabende, Jesujoba Alabi, Thapelo Sindane, Happy Buzaaba, Shamsuddeen Hassan Muhammad, Chris Chinenye Emezue, Perez Ogayo, Anuoluwapo Aremu, Catherine Gitau, Derguene Mbaye, Jonathan Mukiibi, Blessing Sibanda, Bonaventure F. P. Dossou, Andiswa Bukula, Rooweither Mabuya, Allahsera Auguste Tapo, Edwin Munkoh-Buabeng, Victoire Memdjokam Koagne, Fatoumata Ouoba Kabore, Amelia Taylor, Godson Kalipe, Tebogo Macucwa, Vukosi Marivate, Tajuddeen Gwadabe, Mboning Tchiaze Elvis, Ikechukwu Onyenwe, Gratien Atindogbe, Tolulope Adelani, Idris Akinade, Olanrewaju Samuel, Marien Nahimana, Théogène Musabeyezu, Emile Niyomutabazi, Ester Chimhenga, Kudzai Gotosa, Patrick Mizha, Apelete Agbolo, Seydou Traore, Chinedu Uchechukwu, Aliyu Yusuf, Muhammad Abdullahi, Dietrich Klakow
| Challenge: | POS tagging is one of the fundamental steps for many natural language processing (NLP) applications. |
| Approach: | They present AfricaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. |
| Outcome: | The proposed model improves POS tagging performance in unseen languages. |
Toward a Lightweight Solution for Less-resourced Languages: Creating a POS Tagger for Alsatian Using Voluntary Crowdsourcing (L18-1)
Copied to clipboard
| Challenge: | Using a crowdsourcing platform, we collected 18,917 annotations for a less-resourced French regional language, Alsatian. |
| Approach: | They developed a platform that allows people to gather part-of-speech annotations on a variety of corpora and train a first tagger specific to Alsatian. |
| Outcome: | The proposed method is valid for Alsatian and can be adapted to other languages. |
Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging (D18-1)
Copied to clipboard
| Challenge: | Low-resource languages lack manual annotated data to learn basic models such as part-of-speech (POS) taggers. |
| Approach: | They propose a cross-lingual neural part-of-speech tagger that learns from disparate sources of distant supervision in a uniform framework. |
| Outcome: | The proposed model scales to hundreds of low-resource languages without access to gold annotated data. |
Albanian Part-of-Speech Tagging: Gold Standard and Evaluation (L18-1)
Copied to clipboard
| Challenge: | a corpus of more than 31,000 tokens is used for part-of-speech tagging in Albanian . a large number of multi-word units are difficult to tally, especially when they have articles or particles as their first part. |
| Approach: | They propose a gold standard corpus for Albanian part-of-speech tagging and perform evaluation experiments with different statistical taggers. |
| Outcome: | The proposed corpus can accurately represent the syntagmatic aspects of Albanian . the results show that the standard is accurate on both the full and coarse tagsets . |
What data should I include in my POS tagging training set? (2025.findings-emnlp)
Copied to clipboard
| Challenge: | POS tagging is a crucial task for descriptive linguistics and language documentation . POS tags are not available in all languages, but are used for training sets for understudied languages . |
| Approach: | They compare POS tagging with in-context learning, active learning, and random sampling . they find that POS can deliver reasonable results for communities with limited resources . |
| Outcome: | The proposed training set for Indigenous and endangered languages performs better than random sampling. |