Paper Info
Title
Code-Switching Language Modeling with Bilingual Word Embeddings - A Case Study for Egyptian Arabic-English.
Abstract
Code-switching (CS) is a widespread phenomenon among bilingual and multilingual societies. The lack of CS resources hinders the performance of many NLP tasks. In this work, we explore the potential use of bilingual word embeddings for code-switching (CS) language modeling (LM) in the low resource Egyptian Arabic-English language. We evaluate different state-of-the-art bilingual word embeddings approaches that require cross-lingual resources at different levels and propose an innovative but simple approach that jointly learns bilingual word representations without the use of any parallel data, relying only on monolingual and a small amount of CS data. While all representations improve CS LM, ours performs the best and improves perplexity 33.5% relative over the baseline.
Year
DOI
Venue
2019
10.1007/978-3-030-26061-3_17
SPECOM
Field
DocType
ISSN
Perplexity,Code-switching,Computer science,Natural language processing,Artificial intelligence,Egyptian Arabic,Language model
Conference
Proceedings of the 21st International Conference on Speech and Computer (SPECOM'19), Istanbul, Turkey, August 20-25, 2019 https://link.springer.com/book/10.1007/978-3-030-26061-3
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Injy Hamed113.08
Moritz Zhu200.34
Mohamed Elmahdy3134.57
Slim Abdennadher439460.95
Ngoc Thang Vu522035.62