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The effect of domain and diacritics in Yorùbá-English neural machine translation
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In: 18th Biennial Machine Translation Summit ; https://hal.inria.fr/hal-03350967 ; 18th Biennial Machine Translation Summit, Aug 2021, Orlando, United States (2021)
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A Data Augmentation Approach for Sign-Language-To-Text Translation In-The-Wild ...
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The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation ...
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Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages ...
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Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification ...
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Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification ...
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Automatic classification of human translation and machine translation : a study from the perspective of lexical diversity
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Tailoring and Evaluating the Wikipedia for in-Domain Comparable Corpora Extraction ...
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Multilingual and Interlingual Semantic Representations for Natural Language Processing: A Brief Introduction
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In: Computational Linguistics, Vol 46, Iss 2, Pp 249-255 (2020) (2020)
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GeBioToolkit: Automatic Extraction of Gender-Balanced Multilingual Corpus of Wikipedia Biographies ...
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Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yorùbá and Twi ...
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Abstract:
The success of several architectures to learn semantic representations from unannotated text and the availability of these kind of texts in online multilingual resources such as Wikipedia has facilitated the massive and automatic creation of resources for multiple languages. The evaluation of such resources is usually done for the high-resourced languages, where one has a smorgasbord of tasks and test sets to evaluate on. For low-resourced languages, the evaluation is more difficult and normally ignored, with the hope that the impressive capability of deep learning architectures to learn (multilingual) representations in the high-resourced setting holds in the low-resourced setting too. In this paper we focus on two African languages, Yorùbá and Twi, and compare the word embeddings obtained in this way, with word embeddings obtained from curated corpora and a language-dependent processing. We analyse the noise in the publicly available corpora, collect high quality and noisy data for the two languages and ... : 9 pages, 4 tables. Accepted at LREC 2020 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1912.02481 https://dx.doi.org/10.48550/arxiv.1912.02481
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Query Translation for Cross-lingual Search in the Academic Search Engine PubPsych ...
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