82 citations · 180 across the 39 of their papers we have counts for
5 papers · 2 filters
Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging
Hassan Sajjad, Fahim Dalvi, Nadir Durrani +3
Word segmentation plays a pivotal role in improving any Arabic NLP application. Therefore, a lot of research has been spent in improving its accuracy. Off-the-shelf tools, however,…
Neural Machine Translation Training in a Multi-Domain Scenario
Hassan Sajjad, Nadir Durrani, Fahim Dalvi +2
In this paper, we explore alternative ways to train a neural machine translation system in a multi-domain scenario. We investigate data concatenation (with fine tuning), model stac…
What do Neural Machine Translation Models Learn about Morphology?
Yonatan Belinkov, Nadir Durrani, Fahim Dalvi +2
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 lea…
Machine Translation Approaches and Survey for Indian Languages
Nadeem Jadoon Khan, Waqas Anwar, Nadir Durrani
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 basel…
QCRI Machine Translation Systems for IWSLT 16
Nadir Durrani, Fahim Dalvi, Hassan Sajjad +1
This paper describes QCRI's machine translation systems for the IWSLT 2016 evaluation campaign. We participated in the Arabic->English and English->Arabic tracks. We built both Phr…