What is Semantics in Machine Translation (MT)?

Semantics is an important field of study in the translation industry. In simple terms, it explains how the words and sentences are arranged to impress the meanings you intend to.

Since MT is increasingly a part of the context-sensitive translation, semantics also has a role in this tech integration. How so? Semantics in Machine Translation is the basis for training machine learning and performing better results in translation projects. 

Let’s start by summarizing what semantics is and how it can be processed by artificial intelligence.

Semantics Summary

Semantics is a branch of linguistics that deals with meaning through the correct use of combinations of words and sentences.

There are many ways to arrange this puzzle of textual elements, and semantics give us some concepts to work with, such as:

• Denotation

It refers to the literal meaning, objectively. In translation, focusing on denotation can sometimes lead to overly literal translations, but it can be useful if dealing with more traditional content, such as science.

Eg.:

“The ocean is warming faster due to climate change”. (English)

“Los océanos se están calentando rápidamente debido al cambio climático”. (Spanish)

There is no double meaning, right? It’s pretty clear what the sentences affirm to any language.

• Connotation

Connotation involves emotional or cultural associations a word carries beyond its literal meaning. 

These nuances can vary greatly between languages and need careful handling in translation, especially if you are translating literature, e-learning, poetry, etc. Many industries make mistakes on this matter.

Eg.: the word “fox”

In English, the word “fox” literally denotes a wild animal known for its cunning nature. However, it also has connotations when used metaphorically. Calling someone a “fox” can have different connotations, depending on the context:

• Positive Connotation: it implies cleverness or craftiness, like someone strategic or quick-witted.
• Negative Connotation: can have a slightly negative connotation of being sly, deceptive, or untrustworthy.
  

• Hyponymy and Hypernymy

Hyponymy refers to more specific terms under a broader category (hypernym). Hypernymys are like a big umbrella and hyponymys are specific examples under it.

Understanding these relationships helps in choosing the right word in translation.

• Example of Hyponymy: “Rose”, “tulip”, and “daisy” are all hyponyms of the hypernym “flower”. In translation, choosing between a hyponym and a hypernym is context-dependent.
  
  For instance, translating “flower” into French as “fleur” is straightforward. But translating “rose” requires the more specific term “rose”, because the language also has specific words for each.
  
  
• Example of Hypernymy: If the English text says ‘flower” but the source text mentions specific types like roses or lilies, using the generic term ‘fleur” in French could lose some specificity.
  
  
• Polysemy

Polysemy refers to a word that has multiple related meanings. In translation, identifying the correct meaning based on context is essential.

E.g.:

The English word “bank” can mean a financial institution or the side of a river. If translated into French, the financial institution is “banque”, while the riverbank is “rive”. The translator must choose the correct term based on context.

• Ambiguity

Ambiguity occurs when a word, phrase, or sentence can be interpreted in more than one way. Handling ambiguity in translation requires careful consideration of the intended meaning.

E.g.:

"The manager saw the employee with the glasses" is an ambiguous sentence. Does the manager have glasses, or does the employee? In translation, this ambiguity might need to be resolved based on context. 

For instance, when translating to Spanish literally, the ambiguity is still present: "El gerente vio al empleado con los anteojos". Which one is using the glasses?

Why do Semantics Matter in Machine Translation?

Semantics is present in how machines understand and process human language. It’s the key point of training AI to be capable of getting the right meaning of any content.

If you use a CAT tool with bad machine learning processes, semantics issues of all kinds we mentioned before may be present (as problems you’ll need to solve): ambiguity, polysemy, confused use of connotation/denotation, etc.

Getting the right output will depend on the ability of the machine to recognize the context. 

Idiomatic expressions are even more sensible, in most cases, you will need a tuning of human translators, who understand the cultural nuances of both languages.
For example, translating the phrase “break a leg” literally into another language could result in a confusing message. In English, it’s a way to wish someone good luck. But if we translate it into Brazilian Portuguese, you could use “muita merda!”, meaning literally "lots of crap”, but with the right connotation, it is also a very popular expression in the Brazilian theater world to wish good luck.

Understanding the semantic meaning allows MT systems to produce a more accurate and meaningful translation.

Their algorithms utilize semantic analysis techniques to extract meaning from text, which includes processing sentiment analysis, language translation, and question-answering. 

Challenges in Semantics for MT‍

You can imagine that inputting semantics in MT systems is challenging. Each language has hundreds of cultural contexts, idioms, and slang, which make it comprehensible why the machines struggle at times.

Try to translate to another language in Deep L or Google Translator the expression “Let the cat out of the bag!” (meaning “to disclose a secret, often unintentionally”). The results will likely be amusing for being so literal!

While MT systems have improved with AI and deep learning, perfecting semantics is still a work in progress.

In technology and machine translation mastering semantics is key to producing accurate, context-aware translations. While MT tools have come a long way, refining how machines understand and apply semantics is crucial for the future of language technology.