151 citations · 538 across the 29 of their papers we have counts for
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Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling
Nasser Zalmout, Nizar Habash
Morphological tagging is challenging for morphologically rich languages due to the large target space and the need for more training data to minimize model sparsity. Dialectal vari…
Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging
Nasser Zalmout, Nizar Habash
Semitic languages can be highly ambiguous, having several interpretations of the same surface forms, and morphologically rich, having many morphemes that realize several morphologi…
Simple Automatic Post-editing for Arabic-Japanese Machine Translation
Ella Noll, Mai Oudah, Nizar Habash
A common bottleneck for developing machine translation (MT) systems for some language pairs is the lack of direct parallel translation data sets, in general and in certain domains.…
The Impact of Preprocessing on Arabic-English Statistical and Neural Machine Translation
Mai Oudah, Amjad Almahairi, Nizar Habash
Neural networks have become the state-of-the-art approach for machine translation (MT) in many languages. While linguistically-motivated tokenization techniques were shown to have…
An Arabic Dependency Treebank in the Travel Domain
Dima Taji, Jamila El Gizuli, Nizar Habash
In this paper we present a dependency treebank of travel domain sentences in Modern Standard Arabic. The text comes from a translation of the English equivalent sentences in the Ba…