DE eng

Search in the Catalogues and Directories

Hits 1 – 15 of 15

1
Morphological Processing of Low-Resource Languages: Where We Are and What's Next ...
BASE
Show details
2
Pre-Trained Multilingual Sequence-to-Sequence Models: A Hope for Low-Resource Language Translation? ...
BASE
Show details
3
Jump-Starting Item Parameters for Adaptive Language Tests ...
BASE
Show details
4
Unsupervised Morphological Paradigm Completion ...
Jin, Huiming; Cai, Liwei; Peng, Yihui. - : arXiv, 2020
BASE
Show details
5
Predicting Declension Class from Form and Meaning ...
BASE
Show details
6
Predicting declension class from form and meaning
BASE
Show details
7
Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
BASE
Show details
8
Predicting Declension Class from Form and Meaning
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
BASE
Show details
9
UniMorph 3.0: Universal Morphology
In: Proceedings of the 12th Language Resources and Evaluation Conference (2020)
BASE
Show details
10
UniMorph 3.0: Universal Morphology ...
BASE
Show details
11
Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions ...
BASE
Show details
12
Predicting Declension Class from Form and Meaning ...
Williams, Adina; Pimentel, Tiago; Blix, Hagen. - : ETH Zurich, 2020
BASE
Show details
13
The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection ...
Abstract: The SIGMORPHON 2019 shared task on cross-lingual transfer and contextual analysis in morphology examined transfer learning of inflection between 100 language pairs, as well as contextual lemmatization and morphosyntactic description in 66 languages. The first task evolves past years' inflection tasks by examining transfer of morphological inflection knowledge from a high-resource language to a low-resource language. This year also presents a new second challenge on lemmatization and morphological feature analysis in context. All submissions featured a neural component and built on either this year's strong baselines or highly ranked systems from previous years' shared tasks. Every participating team improved in accuracy over the baselines for the inflection task (though not Levenshtein distance), and every team in the contextual analysis task improved on both state-of-the-art neural and non-neural baselines. ... : Presented at SIGMORPHON 2019 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/1910.11493
https://dx.doi.org/10.48550/arxiv.1910.11493
BASE
Hide details
14
Modeling Color Terminology Across Thousands of Languages ...
BASE
Show details
15
Marrying Universal Dependencies and Universal Morphology ...
BASE
Show details

Catalogues
0
0
0
0
0
0
0
Bibliographies
0
0
0
0
0
0
0
0
0
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
15
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern