{"title":"构建一个基于神经网络的英语-阿拉伯语转换模块","authors":"Rasha Al Dam, A. Guessoum","doi":"10.1109/ICMWI.2010.5648157","DOIUrl":null,"url":null,"abstract":"This paper presents a Transfer Module for an English-to-Arabic Machine Translation System (MTS) using an English-to-Arabic Bilingual Corpus. We propose an approach to build a transfer module by building a new transfer-based system for machine translation using Artificial Neural Networks (ANN). The idea is to allow the ANN-based transfer module to automatically learn correspondences between source and target language structures using a large set of English sentences and their Arabic translations. The paper presents the methodology for corpus building. It then introduces the approach that has been followed to develop the transfer module. It finally presents the experimental results which are very encouraging.","PeriodicalId":404577,"journal":{"name":"2010 International Conference on Machine and Web Intelligence","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Building a neural network-based English-to-Arabic transfer module from an unrestricted domain\",\"authors\":\"Rasha Al Dam, A. Guessoum\",\"doi\":\"10.1109/ICMWI.2010.5648157\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a Transfer Module for an English-to-Arabic Machine Translation System (MTS) using an English-to-Arabic Bilingual Corpus. We propose an approach to build a transfer module by building a new transfer-based system for machine translation using Artificial Neural Networks (ANN). The idea is to allow the ANN-based transfer module to automatically learn correspondences between source and target language structures using a large set of English sentences and their Arabic translations. The paper presents the methodology for corpus building. It then introduces the approach that has been followed to develop the transfer module. It finally presents the experimental results which are very encouraging.\",\"PeriodicalId\":404577,\"journal\":{\"name\":\"2010 International Conference on Machine and Web Intelligence\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Machine and Web Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMWI.2010.5648157\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Machine and Web Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMWI.2010.5648157","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Building a neural network-based English-to-Arabic transfer module from an unrestricted domain
This paper presents a Transfer Module for an English-to-Arabic Machine Translation System (MTS) using an English-to-Arabic Bilingual Corpus. We propose an approach to build a transfer module by building a new transfer-based system for machine translation using Artificial Neural Networks (ANN). The idea is to allow the ANN-based transfer module to automatically learn correspondences between source and target language structures using a large set of English sentences and their Arabic translations. The paper presents the methodology for corpus building. It then introduces the approach that has been followed to develop the transfer module. It finally presents the experimental results which are very encouraging.