Scaling up a Hybrid MT System: From low to full resources

This article describes a hybrid approach to machine translation (MT) that is inspired by the rule-based, statistical, example-based, and other hybrid machine translation approaches currently used or described in academic literature. It describes how the approach was implemented for language pairs using only limited monolingual resources and hardly any parallel resources (the METIS-II system), and how it is currently implemented with rich resources on both the source and target side as well as rich parallel data (the PaCo-MT system). We aim to illustrate that a similar paradigm can be used, irrespectively of the resources available, but of course with an impact on translation quality.

Tourist’s Translator based on Digital Image Processing and Hybrid Translation

Being a tourist in a foreign country is not easy when it comes to getting familiar with the local language. From reading signboards to getting unfairly charged while shopping, booking cabs and hotels for their stay, roaming around for sightseeing, communicating with the locals, everything requires an understanding of the local language. Nowadays, everybody has a smartphone, which proves to be the most helpful tool for travelling purposes. We aim to build up an Android application that will be capable of translating text, voice, and also textual information written on signboards from native language to the desired language, users have to use their smartphone camera for signboard translation. We are using a hybrid machine translation approach for language translation. Text recognition from the captured images of signboards is done with the help of digital image processing. The purpose of this work is to reduce the failure of any single machine translation approach and to reduce miscommunication between tourists and the local people.

Hybrid Translation with Classification: Revisiting Rule-Based and Neural Machine Translation

This paper proposes a hybrid machine-translation system that combines neural machine translation with well-developed rule-based machine translation to utilize the stability of the latter to compensate for the inadequacy of neural machine translation in rare-resource domains. A classifier is introduced to predict which translation from the two systems is more reliable. We explore a set of features that reflect the reliability of translation and its process, and training data is automatically expanded with a small, human-labeled dataset to solve the insufficient-data problem. A series of experiments shows that the hybrid system’s translation accuracy is improved, especially in out-of-domain translations, and classification accuracy is greatly improved when using the proposed features and the automatically constructed training set. A comparison between feature- and text-based classification is also performed, and the results show that the feature-based model achieves better classification accuracy, even when compared to neural network text classifiers.

A Survey on Hybrid Machine Translation

Machine translation has gradually developed in past 1940’s.It has gained more and more attention because of effective and efficient nature. As it makes the translation automatically without the involvement of human efforts. The distinct models of machine translation along with “Neural Machine Translation (NMT)” is summarized in this paper. Researchers have previously done lots of work on Machine Translation techniques and their evaluation techniques. Thus, we want to demonstrate an analysis of the existing techniques for machine translation including Neural Machine translation, their differences and the translation tools associated with them. Now-a-days the combination of two Machine Translation systems has the full advantage of using features from both the systems which attracts in the domain of natural language processing. So, the paper also includes the literature survey of the Hybrid Machine Translation (HMT).