Morphological Word Embeddings for Arabic Neural Machine Translation in Low-Resource Settings (2018 · 25 citations)
Standard word embeddings are poor for rare words, and Arabic inflects heavily. The authors change the word2vec skip-gram objective so that the context is predicted from the word vector concatenated with a weighted average of lemma vectors, with lemmas from MADAMIRA. They test the embeddings on an Arabic word…
Comparing Pipelined and Integrated Approaches to Dialectal Arabic Neural Machine Translation (2019 · 5 citations)
The paper asks how to translate Arabic into English when the dialect of the input sentence is unknown. One option is a pipeline: identify the dialect first, then send the sentence to a translation model tuned for that dialect. The other is a single multi-dialectal Transformer used for every input. The authors use LDC…

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