Different Machine Translation Models in NLP

Hello Everyone!

Hope you are having a great Quarantine time : )

Just to help you make the best out of your Quarantine time, here we have came up with an Introduction to one of the most important and hot topic in NLP: Machine Translation and its types.

So let's dive deep into this without any further delay.

Let's begin by first having an introduction to Machine Translation (MT). In simple terms, Machine Translation is the process of converting the text in a source language to a required target language.

The pictorial representation of the Machine Translation, in an abstract form, can be seen in the image below:

Given a sequence of text in a source language, there is no one single best translation of that text to another language. This is because of the natural ambiguity and flexibility of human language, especially with most of the Indian Languages that are rarely spoken in other countries.

Traditionally, Natural Language Processing of both spoken and written language has been regarded as consisting of the following stages:

1. Phonology and Phonetics: (Processing of sound)

2. Morphology: (Processing of word forms)

3. Lexicon: (Storage of words and associated knowledge)

4. Parsing: (Processing of structure)

5. Semantics: (Processing of meaning)

6. Pragmatics: (Processing of user intention, modeling, etc.)

7. Discourse: (Processing of connected text)

We have already covered the various forms of natural ambiguities associated with each of the stages of natural language processing.

Here is the pictorial representation of Bernard Vauquois' pyramid, showing comparative depths of intermediary representation, interlingual machine translation at the peak, followed by transfer-based, then direct translation.

The various types of Machine Translation are:

• Statistical Machine Translation (SMT)

• Rule-based Machine Translation (RBMT)

• Hybrid Machine Translation (HMT)

• Neural Machine Translation (NMT)

The detailed relationship between various Machine Translation techniques can be seen below:

Let's go through each of them one at a time to get an overview of the need for its existence and which form is to be chosen when and for what purpose.

1. Statistical Machine Translation (SMT):

SMT is mostly based on Information Theory which studies the quantification, storage, and communication of information.

Supervised and unsupervised machine learning algorithms are used for building the statistical models.

The statistical models consist of statistical information such as co-relation between the SL and TL and well formed sentences. During the translation statistical models helped to find the best translation of the source text.

The most abstract view of Statistical Machine Translation can be understood from the image below:

The various types of Statistical based MT models are:

• Hierarchical phrase-based translation

• Syntax-based translation

• Phrase-based translation

• Word-based translation

We are going to cover each of them in a bit more detail in our upcoming articles as here the focus is on explaining all the Machine Translation models. So let's move on to the next model in our list i.e. Rule-Based Machine Translation.

2. Rule-Based Machine Translation:

Rule-Based Machine Translation (RBMT) relies on innumerous built-in linguistic rules and millions of bilingual dictionaries for each language pair.

The below given example will help you understand the basic concept involved in Rule-Based Machine Translation:

3. Hybrid Machine Translation (HMT)

The various approaches of HMT are:

1. Multi-engine: In this approach the focus is on achieving parallelism by running multiple MT systems in parallel and getting the output by combining the outputs of various sub-systems.

2. Statistical rule generation: In this approach, statistical data is used to generate various lexical and syntactic rules.It is also a very time-savvy as it extracts some of the rules directly from the training data.

3. Multi-Pass: In this approach, the input is processed multiple times in sequential order. One of the most common techniques that uses this approach is Pre-processing of data.

4. Confidence-Based: this approach is different from all the above ones as in this, a confidence matrix is produced for each of the translated sentence from which a decision can be made whether a secondary translation technology is required or the output from the first one is sufficient, unlike in others, where only a single translation technology is used.

Some of the popular machine translation systems which employ hybrid machine translation methods are PROMT, SYSTRAN and Omniscien Technologies.

4. Neural Machine Translation (NMT):

In Neural Machine Translation (NMT), we make use of a neural network model to learn a statistical model for machine translation.

One of the key advantages of NMT over SMT is that in NMT, a single system can be trained directly on source as well as target text thereby removing the dependency on a pipeline of specialized systems as that in SMT.

Conclusion:

We hope that this article has helped you understand the basic concepts involved in the domain of Machine Translation, which is one of the fastest emerging domain in the field of Machine Learning. In the upcoming post, we will be discussing the various types of Statistical Machine Translation, in much more detail.

Wish you a productive and a safe stay!!

Happy Quarantine : )

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