DeepAI AI Chat
Log In Sign Up

Egyptian Arabic to English Statistical Machine Translation System for NIST OpenMT'2015

by   Hassan Sajjad, et al.

The paper describes the Egyptian Arabic-to-English statistical machine translation (SMT) system that the QCRI-Columbia-NYUAD (QCN) group submitted to the NIST OpenMT'2015 competition. The competition focused on informal dialectal Arabic, as used in SMS, chat, and speech. Thus, our efforts focused on processing and standardizing Arabic, e.g., using tools such as 3arrib and MADAMIRA. We further trained a phrase-based SMT system using state-of-the-art features and components such as operation sequence model, class-based language model, sparse features, neural network joint model, genre-based hierarchically-interpolated language model, unsupervised transliteration mining, phrase-table merging, and hypothesis combination. Our system ranked second on all three genres.


page 1

page 2

page 3

page 4


QCRI Machine Translation Systems for IWSLT 16

This paper describes QCRI's machine translation systems for the IWSLT 20...

First Result on Arabic Neural Machine Translation

Neural machine translation has become a major alternative to widely used...

A Recipe for Arabic-English Neural Machine Translation

In this paper, we present a recipe for building a good Arabic-English ne...

Morphological Constraints for Phrase Pivot Statistical Machine Translation

The lack of parallel data for many language pairs is an important challe...

Simplifying the Bible and Wikipedia Using Statistical Machine Translation

I started this work with the hope of generating a text synthesizer (like...

Contribution au Niveau de l'Approche Indirecte à Base de Transfert dans la Traduction Automatique

In this thesis, we address several important issues concerning the morph...

Supporting Language Learners with the Meanings Of Closed Class Items

The process of language learning involves the mastery of countless tasks...