Large-Scale Machine Translation between Arabic and Hebrew: Available Corpora and Initial Results

By Yonatan Belinkov, James R. Glass. 2016

16 citations recorded by the source corpus.

Machine translation between Arabic and Hebrew has so far been limited by a lack of parallel corpora, despite the political and cultural importance of this language pair. Previous work relied on manually-crafted grammars or pivoting via English, both of which are unsatisfactory for building a scalable and accurate MT system. In this work, we compare standard phrase-based and neural systems on Arabic-Hebrew translation. We experiment with tokenization by external tools and sub-word modeling by character-level neural models, and show that both methods lead to improved translation performance, with a small advantage to the neural models.

Subfields: Artificial Intelligence, Natural Language Processing, Machine Translation

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Citations in the corpus

What do Neural Machine Translation Models Learn about Morphology? (2017, methodology): “Here we focus on a character CNN which has been used in language modeling and machine translation (Kim et al., 2015; Belinkov and Glass, 2016; Costa-juss`a and Fonollosa, 2016; Jozefow-icz et al., 2016; Sajjad et al., 2017).”
On internal language representations in deep learning: an analysis of machine translation and speech recognition (2018, methodology): “Lastly, neural machine translation facilitates the use of character-aware representations, where a word may be represented as a sequence of characters that is processed in a sub-network [27, 82, 209, 219, 291, 330].”
Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder (2017, methodology): “…popular in Neural MT, for addressing the rare word problem – and they have been used previously also to beneﬁt MT for morphologically rich (Luong et al., 2010; Belinkov and Glass, 2016; Costa-juss ` a and Fonollosa, 2016) and closely related languages (Durrani et al., 2010; Sajjad et al., 2013).”