DeepAI AI Chat
Log In Sign Up

Part of Speech and Universal Dependency effects on English Arabic Machine Translation

06/01/2021
by   Ofek Rafaeli, et al.
0

In this research paper, I will elaborate on a method to evaluate machine translation models based on their performance on underlying syntactical phenomena between English and Arabic languages. This method is especially important as such "neural" and "machine learning" are hard to fine-tune and change. Thus, finding a way to evaluate them easily and diversely would greatly help the task of bettering them.

READ FULL TEXT

page 1

page 2

page 3

page 4

09/14/2017

Towards an Arabic-English Machine-Translation Based on Semantic Web

Communication tools make the world like a small village and as a consequ...
11/22/2022

ArzEn-ST: A Three-way Speech Translation Corpus for Code-Switched Egyptian Arabic - English

We present our work on collecting ArzEn-ST, a code-switched Egyptian Ara...
02/06/2023

Context-Gloss Augmentation for Improving Arabic Target Sense Verification

Arabic language lacks semantic datasets and sense inventories. The most ...
12/10/2019

Homograph Disambiguation Through Selective Diacritic Restoration

Lexical ambiguity, a challenging phenomenon in all natural languages, is...
10/26/2020

Is it Great or Terrible? Preserving Sentiment in Neural Machine Translation of Arabic Reviews

Since the advent of Neural Machine Translation (NMT) approaches there ha...
11/16/2019

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...
11/06/2015

Multi-lingual Geoparsing based on Machine Translation

Our method for multi-lingual geoparsing uses monolingual tools and resou...