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What do Neural Machine Translation Models Learn about Morphology?
Neural machine translation (MT) models obtain state-of-the-art performance while maintaining a simple, end-to-end architecture. However, little is known about what these models learn about source andExpand
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Verifiably Effective Arabic Dialect Identification
Several recent papers on Arabic dialect identification have hinted that using a word unigram model is sufficient and effective for the task. However, most previous work was done on a standard fairlyExpand
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Robust Classification of Crisis-Related Data on Social Networks Using Convolutional Neural Networks
The role of social media, in particular microblogging platforms such as Twitter, as a conduit for actionable and tactical information during disasters is increasingly acknowledged. However,Expand
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The AMARA Corpus: Building Parallel Language Resources for the Educational Domain
This paper presents the AMARA corpus of on-line educational content: a new parallel corpus of educational video subtitles, multilingually aligned for 20 languages, i.e. 20 monolingual corpora and 190Expand
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Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging Tasks
While neural machine translation (NMT) models provide improved translation quality in an elegant, end-to-end framework, it is less clear what they learn about language. Recent work has startedExpand
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Applications of Online Deep Learning for Crisis Response Using Social Media Information
During natural or man-made disasters, humanitarian response organizations look for useful information to support their decision-making processes. Social media platforms such as Twitter have beenExpand
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Rapid Classification of Crisis-Related Data on Social Networks using Convolutional Neural Networks
The role of social media, in particular microblogging platforms such as Twitter, as a conduit for actionable and tactical information during disasters is increasingly acknowledged. However,Expand
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Findings of the First Shared Task on Machine Translation Robustness
We share the findings of the first shared task on improving robustness of Machine Translation (MT). The task provides a testbed representing challenges facing MT models deployed in the real world,Expand
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Integrating an Unsupervised Transliteration Model into Statistical Machine Translation
We investigate three methods for integrating an unsupervised transliteration model into an end-to-end SMT system. We induce a transliteration model from parallel data and use it to translate OOVExpand
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Identifying and Controlling Important Neurons in Neural Machine Translation
Neural machine translation (NMT) models learn representations containing substantial linguistic information. However, it is not clear if such information is fully distributed or if some of it can beExpand
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