Abstract
Resources for the non-English languages are scarce and this paper addresses this problem in the context of machine translation, by automatically extracting parallel sentence pairs from the multilingual articles available on the Internet. In this paper, we have used an end-to-end Siamese bidirectional recurrent neural network to generate parallel sentences from comparable multilingual articles in Wikipedia. Subsequently, we have showed that using the harvested dataset improved BLEU scores on both NMT and phrase-based SMT systems for the low-resource language pairs: English--Hindi and English--Tamil, when compared to training exclusively on the limited bilingual corpora collected for these language pairs.
Abstract (translated)
非英语语言的资源非常稀少，本文通过自动从互联网上提供的多语言文章中提取平行句对来解决机器翻译环境中的这个问题。在本文中，我们使用了一个端到端的连体双向递归神经网络来生成维基百科中可比较的多语言文章的并行句子。随后，我们发现使用收集的数据集改善了NMT和基于短语的SMT系统上的BLEU分数，这些分数低于资源语言对：英语 - 印地语和英语 - 泰米尔语，与仅限于有限双语语料库的培训相比收集这些语言对。
URL
https://arxiv.org/abs/1806.09652
