Work

Domain and Dialect Adaptation for Machine Translation into Egyptian Arabic

Year: 2014

Type: conference-paper

Abstract: In this paper, we present a statistical machine translation system for English to Dialectal Arabic (DA), using Modern Standard Arabic (MSA) as a pivot.We create a core system to translate from English to MSA using a large bilingual parallel corpus.Then, we design two separate pathways for translation from MSA into DA: a two-step domain and dialect adaptation system and a one-step simultaneous domain and dialect adaptation system.Both variants of the adaptation systems are trained on a 100k sentence tri-parallel corpus of English, MSA, and Egyptian Arabic generated by a rule-based transformation.We test our systems on a held-out Egyptian Arabic test set from the 100k sentence corpus and we achieve our best performance using the two-step domain and dialect adaptation system with a BLEU score of 42.9.

Source: Arabic Natural Language Processing Workshop (WANLP)

DOI: 10.3115/v1/w14-3627

Authors: Serena Jeblee, Weston Feely, Houda Bouamor, Alon Lavie, Nizar Habash, Kemal Oflazer

Institutions: Carnegie Mellon University, Carnegie Mellon University Qatar, New York University Abu Dhabi

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W2251882277

FWCI: 1.767

Cites: 33

Cited by: 27

Related to: 10