The language industry is having a moment. The ongoing global health crisis has forced organizations to break down borders and support a global remote workforce, requiring more cross-language interactions and coordination than ever before. At the same time, technological innovations in the language translation industry are at an all time high. We've never before had access to such sophisticated technology tools to manage translation processes. I predict it's going to be an exciting year in the industry, with an unprecedented level of innovation.
Statistical machine translation models have made great progress in improving the translation quality. However, the existing models predict the target translation with only the source- and target-side local context information. In practice, distinguishing good translations from bad ones does not only depend on the local features, but also rely on the global sentence-level information. In this paper, we explore the source-side global sentence-level features for target-side local translation prediction. We propose a novel bilingually-constrained chunk-based convolutional neural network to learn sentence semantic representations. With the sentence-level feature representation, we further design a feed-forward neural network to better predict translations using both local and global information. The large-scale experiments show that our method can obtain substantial improvements in translation quality over the strong baseline: the hierarchical phrase-based translation model augmented with the neural network joint model.
Knowledge graph embedding refers to projecting entities and relations in knowledge graph into continuous vector spaces. Current state-of-the-art models are translation-based model, which build embeddings by treating relation as translation from head entity to tail entity. However, previous models is too strict to model the complex and diverse entities and relations(e.g.
Google Translate provides a multilingual machine-translation service by automatically translating one written language to another. Google translate is allegedly limited in its accuracy in translation, however. This study investigated the accuracy of Google Chinese-to-English translation from the perspectives of formality and cohesion with two comparisons: Google translation with human expert translation, and Google translation with Chinese source language. The text sample was a collection of 289 spoken and written texts excerpts from the Selected Works of Mao Zedong in both Chinese and English versions. Google translate was used to translate the Chinese texts into English. These texts were analyzed by the automated text analysis tools: the Chinese and English LIWC, and the Chinese and English Coh-Metrix. Results of Pearson correlations on formality and cohesion showed Google English translation was highly correlated with both human English translation and the original Chinese texts.
Machine translation has been around for many years. However, it wasn't until Google, Microsoft and others began developing machine translation that it grew into a serious competitive alternative to human translation. As a result, machine translation has made more progress in the last 10 years than the previous 50 years. Today, machine translation is used to produce billions of words daily and is fast closing in on human translation quality. At the heart of the improvement in machine translation quality is artificial intelligence.