While you can manually analyze data, using AI tools is a much faster way of handling these things. Via a tool, you can analyze the text more quickly and detect plagiarism (with a much higher accuracy rate than with traditional plagiarism checkers). In this post, we’ll explore comparing translations using AI tools!
These language-based tools can also be used for translation. Sure, they’ll never pick up subtleties of human interaction, but they’re quick, universally available, and efficient at what they do. Not to mention that quality translators are incredibly expensive (very high hourly rate), while these tools are available to anyone. Even if they’re not as good as a good translator, they’re better than a bad one (spoiler alert, not all translators/interpreters are as proficient as you think).
With this in mind and without further ado, here’s what you should know about using AI tools for document comparison and translation.
How Do AI Tools Compare Documents?
You need quality document comparison software to analyze documents to get some basic statistics, extract the crucial phrases (keywords), or look for plagiarism (even editing).
How does document comparison software even work? Here’s how this process works.
First, you get to determine document similarity through a three-step process:
- Preprocessing: This is the tokenization of text into words and phrases by removing all the unnecessary formatting and punctuation.
- Vectorization: In this stage, you convert the text into a numerical representation. This way, you make it easier for the AI algorithm to understand.
- Comparison: Via the algorithm described in the previous section, comparing two texts is handled mathematically.
While this is the outline of the process, the truth is that the bulk of the procedure usually depends on the type of AI tool used.
In Which Scenarios Can Document Comparison be Used?
To understand why this matters, you need to consider a few practical scenarios in which you’ll need this.
- First, you want to use document comparison for plagiarism detection. Remember that this is not always as simple because many tools can (slightly) randomize the text. Changing a few words doesn’t mean the text is no longer plagiarized; it just means you need a more sophisticated tool to detect this.
- Version control is incredibly important, especially for files on which many people collaborate. This way, you can highlight the differences and track the progression (evolution of the document in question).
- What if someone sends you a contract to sign, only to pretend it was a different one? With a tool capable of accurate document comparison, you could prove them wrong (and yourself innocent) in minutes. This entire conundrum is also why smart contracts and blockchain are such game-changer.
- Nothing could ruin your efforts quicker than duplicate content in content marketing. This means detecting it may make a difference between your campaign’s success and failure.
As you can see, the situations are numerous, and it’s easy to see how any business could need this approach.
What Are the Two Approaches to AI Translation?
There are two processes for producing a translation:
- Neural machine translation (NMT)
- Phrase-based machine translation (PBMT)
These two are vastly different and operate on vastly different processes. First, they differ in various technical aspects, like their architecture. NMT is heavily based on deep learning architectures, which helps them understand phrases far better.
On the other hand, PBTM models approach the language more statistically. This also means that they can more quickly shift through different translation variations (finding the optimal solution in the process).
Regarding translation quality, it’s not clear-cut where you’ll gain more. First, While PBMT is still believed to be superior, NMT is advancing at an unprecedented rate. The majority of experts believe that with more time, improvements to algorithms, and more training data, PBTM might drastically overtake its competitors. This is especially visible regarding complex issues like linguistic and cultural adaptations.
Lastly, regarding adaptability to new languages, NMT is far ahead. While PBMT may be just as efficient, it requires manual input, significantly slowing down the process. Since speed is one of the winning factors in this field, it’s hard to
How Do AI Tools Produce a Translation?
In terms of the process behind this translation, things go something like this:
NMT
- Preprocessing: The text is cleaned, normalized, and language-specific preprocessing is handled as efficiently as possible.
- Training: The corpora from the source and parallel text are collected and used to train the NMT model.
- Interface: The user can now use this (fully-trained) model to translate the text to the target language.
PBMT
- Preprocessing: This part is fairly similar to the one in NMT, with the difference that, in the end, sentences are tokenized.
- Training: In this stage, the system aligns the texts, extracts phrase pairs (from the source language and translated language), and creates a statistical translation model.
- Decoding: The system operates on the statistical model, finding the most probable translation (based on all the factors weighed in).
In the end, remember that there are different NMT and PBMT tools. While these tools have different UIs, these are the processes that take place below the surface.
What Are the Limitations of AI Tools?
AI tools are not infallible. They may produce silly mistakes, especially when faced with ambiguity or an abstract text.
In other words, by their design, they’re mostly devised for basic translation. Imagine a scenario where you’re a tourist asking for directions or trying to lead a rudimentary communication with the local populace. This is what they’re made for, and this is where they show the best results.
Using them for something more complex (like analyzing a philosophical or religious text) is bound to give you some disappointing results. This is not it failing; it’s you expecting too much.
Wrapping Up
While document comparison and translation tools have existed for quite a while, with the latest AI development, they’re becoming more dependable than ever. This is important because these tasks depend on accuracy and recognizing subtle differences (that are significant enough to change the text/document). That said, both of these tool types work best with human oversight.
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