• Publications
  • Influence
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
  • 182
  • 17
  • PDF
Farasa: A Fast and Furious Segmenter for Arabic
In this paper, we present Farasa, a fast and accurate Arabic segmenter. Our approach is based on SVM-rank using linear kernels. We measure the performance of the segmenter in terms of accuracy andExpand
  • 117
  • 16
  • PDF
A Joint Sequence Translation Model with Integrated Reordering
We present a novel machine translation model which models translation by a linear sequence of operations. In contrast to the "N-gram" model, this sequence includes not only translation but alsoExpand
  • 114
  • 11
  • PDF
Urdu Word Segmentation
Word Segmentation is the foremost obligatory task in almost all the NLP applications where the initial phase requires tokenization of input into words. Urdu is amongst the Asian languages that faceExpand
  • 68
  • 7
  • PDF
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
  • 79
  • 6
  • PDF
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
  • 21
  • 4
  • PDF
Machine Translation Approaches and Survey for Indian Languages
In this study, we present an analysis regarding the performance of the state-of-art Phrase-based Statistical Machine Translation (SMT) on multiple Indian languages. We report baseline systems onExpand
  • 25
  • 4
  • PDF
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
  • 87
  • 3
  • PDF
Can Markov Models Over Minimal Translation Units Help Phrase-Based SMT?
The phrase-based and N-gram-based SMT frameworks complement each other. While the former is better able to memorize, the latter provides a more principled model that captures dependencies acrossExpand
  • 67
  • 3
  • PDF
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
  • 53
  • 3
  • PDF