How to Become a Translator - 2

4) THE TRANSLATION TOOLS

A lot of translation tools were developed. There are two kinds of tools: the machine translation software and the translation help software.

I am going to tell you about machine translation to show you that you have nothing to fear. It will take some time before they replace a human translator.

4.1) Machine Translation

Machine translation, sometimes referred to by the abbreviation MT, is a sub-field of computational linguistics that investigates the use of computer software to translate text or speech from one natural language to another. At its basic level, MT performs simple substitution of words in one natural language for words in another. Using corpus techniques, more complex translations may be attempted, allowing for better handling of differences in linguistic typology, phrase recognition, and translation of idioms, as well as the isolation of anomalies.

Current machine translation software often allows for customisation by domain or profession (such as weather reports) - improving output by limiting the scope of allowable substitutions. This technique is particularly effective in domains where formal or formulaic language is used. It follows then that machine translation of government and legal documents more readily produces usable output than conversation or less standardised text.
Improved output quality can also be achieved by human intervention: for example, some systems are able to translate more accurately if the user has unambiguously identified which words in the text are names. With the assistance of these techniques, MT has proven useful as a tool to assist human translators, and in some cases can even produce output that can be used "as is". However, current systems are unable to produce output of the same quality as a human translator, particularly where the text to be translated uses casual language

Recently, Internet has emerged as global information infrastructure, revolutionizing access to any information, as well as fast information transfer and exchange. Using Internet and e-mail technology, people need to communicate rapidly over long distances across continent boundaries. Not all of these Internet users, however, can use their own language for global communication to different people with different languages. Therefore, using machine translation software, people can possibly communicate and contact one to another around the world in their own mother tongue, in the near future.

Translation Process

The translation process may be stated as:

- decoding the meaning of the source text; and
- Re-encoding this meaning in the target language.

Behind this ostensibly simple procedure lies a complex cognitive operation. To decode the meaning of the source text in its entirety, the translator must interpret and analyse all the features of the text, a process that requires in-depth knowledge of the grammar, semantics, syntax, idioms, etc., of the source language, as well as the culture of its speakers. The translator needs the same in-depth knowledge to re-encode the meaning in the target language.
Therein lies the challenge in machine translation: how to program a computer that will "understand" a text as a person does, and that will "create" a new text in the target languages that "sounds" as if it has been written by a person.

This problem may be approached in a number of ways.

4.1.2) Approaches

Machine translation can use a method based on linguistic rules, which means that words will be translated in a linguistic way - the most suitable (orally speaking) words of the target language will replace the ones in the source language.

It is often argued that the success of machine translation requires the problem of natural language understanding to be solved first.

Generally, rule-based methods parse a text, usually creating an intermediary, symbolic representation, from which the text in the target language is generated. According to the nature of the intermediary representation, an approach is described as interlingual machine translation or transfer based machine translation. These methods require extensive lexicons with morphological, syntactic, and semantic information, and large sets of rules.

Given enough data, machine translation programs often work well enough for a native speaker of one language to get the approximate meaning of what is written by the other native speaker. The difficulty is getting enough data of the right kind to support the particular method. For example, the large multilingual corpus of data needed for statistical methods to work is not necessary for the grammar-based methods. But then, the grammar methods need a skilled linguist to carefully design the grammar that they use.
To translate between closely related languages, a technique referred to as shallow transfer machine translation may be used.

4.1.2.1) Rule-based

The rule-based machine translation paradigm includes transfer-based machine translation, interlingual machine translation and dictionary-based machine translation paradigms.

4.1.2.1.1) Transfer-based machine translation

Overview

Both transfer-based and interlingua-based machine translation have the same idea: to make a translation it is necessary to have an intermediate representation that captures the "meaning" of the original sentence in order to generate the correct translation. In interlingua-based MT this intermediate representation must be independent of the languages in question, whereas in transfer-based MT, it has some dependence on the language pair involved.
The way in which transfer-based machine translation systems work varies substantially, but in general they follow the same pattern: they apply sets of linguistic rules which are defined as correspondences between the structure of the source language and that of the target language. The first stage involves analysing the input text for morphology and syntax (and sometimes semantics) to create an internal representation. The translation is generated from this representation using both bilingual dictionaries and grammatical rules.
It is possible with this translation strategy to obtain fairly high quality translations, with accuracy in the region of 90% (although this is highly dependent on the language pair in question - for example the distance between the two).

How it works

In a rule-based machine translation system the original text is first analysed morphologically and syntactically in order to obtain a syntactic representation. This representation can then be refined to a more abstract level putting emphasis on the parts relevant for translation and ignoring other types of information. The transfer process then converts this final representation (still in the original language) to a representation of the same level of abstraction in the target language. These two representations are referred to as "intermediate" representations. From the target language representation, the stages are then applied in reverse.

Analysis and transformation:
Various methods of analysis and transformation can be used before obtaining the final result. Along with these statistical approaches may be augmented generating hybrid systems. The methods which are chosen and the emphasis depends largely on the design of the system, however, most systems include at least the following stages:

Morphological analysis:
Surface forms of the input text are classified as to part-of-speech (e.g. noun, verb, etc.) and sub-category (number, gender, tense, etc.) All of the possible "analyses" for each surface form are typically outputted at this stage, along with the lemma of the word.

Lexical categorisation:
In any given text some of the words may have more than one meaning, causing ambiguity in analysis. Lexical categorisation looks at the context of a word to try and determine the correct meaning in the context of the input. This can involve part-of-speech tagging and word sense disambiguation.

Lexical transfer:
This is basically dictionary translation, the source language lemma (perhaps with sense information) is looked up in a bilingual dictionary and the translation is chosen.

Structural transfer:
While the previous stages deal with words, this stage deals with larger constituents, for example phrases and chunks. Typical features of this stage include concordance of gender and number, and re-ordering of words or phrases.

Morphological generation:
From the output of the structural transfer stage, the target language surface forms are generated.

Transfer types

One of the main features of transfer based machine translation systems is a phase that "transfers" an intermediate representation of the text in the original language to an intermediate representation of text in the target language. This can work at one of two levels of linguistic analysis , or somewhere in between. The levels are:

Superficial transfer (or syntactic):
This level is characterised by transferring "syntactic structures" between the source and target languages. It is suitable for languages in the same family or of the same type, for example in the Romance languages between Spanish, Catalan, French, Italian, etc.

Deep transfer (or semantic):
This level constructs a semantic representation that is dependent on the source language. This representation can consist of a series of structures which represent the meaning. In these transfer systems predicates are typically produced. The translation also typically requires structural transfer. This level is used to translate between more distantly related languages, or languages which have no genetic relationship at all (e.g. Spanish-English or Spanish-Basque, etc.)

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