O'Reilly logo

Hands-On Natural Language Processing with Python by Rajalingappaa Shanmugamani, Rajesh Arumugam

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Summary

In this chapter, we described the different commonly used machine translation methods with particular focus on neural machine translation. We briefly described classic statistical machine translation in the context of lexical alignment models. We showed a simple example for building an SMT alignment model using NLTK. SMT can be used when we have a large corpus of bilingual data.

The main shortcoming of such models is that they do not generalize well to domains (or contexts) other than the one in which they were trained. Recently, deep neural networks have become the popular approach to machine translation, mainly because of their effectiveness in producing close to human-level translations. We described in detail how to build an NMT ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required