An Overview of Cross-Lingual Language Models
An Overview of Cross-Lingual Language Models
Our lives have become increasingly intertwined with computers and machines. So much so that some of our communication is no longer through machines, it is with machines. It is therefore extremely important that machines can understand us.
We call NLP Natural Language Processing, which is the general term for the study of how machines understand and analyze our language for many different tasks. Combining linguistics, computer science, artificial intelligence, and machine learning, this extremely broad field holds great promise. From text recognition (build with Cameralyze) to digital translations, many developments have taken place in our lives in this way.
But how do these technologies work in different languages and how do machines make connections between languages? How does NLP work? How can you use these technologies in the most beneficial way for yourself and your business? The answer is in our article.
In this article you will find the following topics:
· What Is Cross-Lingual Language
· NLP With Other Languages: Non-English NLP
· Multilingual NLP Tasks
· Cross-Lingual Language Models in Machine Learning
· mBbert
· XLM
· Multifit
What Is Cross-Lingual Language?
Although studies in the field of NLP, or Natural Language Processing, have been carried out for years, all studies in the field have been in English. The vast majority of sentences that machines could understand, or rather perceive and encode, were in English. Breaking this English-dominated orientation and enabling machines to perceive and encode almost every language that exists in a global manner is called Cross-Lingual Language studies.
Cross-Lingual Language NLP is a very difficult and complex process. The reason for this complexity and difficulty lies in the fundamental differences between languages. All of the more than 5000 languages spoken around the world have different rules and vectors. So machines need to be trained to recognize these languages, to make sense out of shapes.
For this reason, although there are language recognition systems that work quite well in different languages today, the process is still evolving and this takes time.
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Why Cross-Lingual Language is Important
The development of Cross-Lingual Language is of utmost importance for the perception of writing in different languages that can be used around the world and to be able to offer translation activities to improve the communication of people all over the world with machines.
NLP With Other Languages: Non-English NLP
As the world we live in globalizes, technologies that can only analyze English are far from reality. For important tasks to be performed by computers, machines need to understand many languages and be able to scan documents in these languages. This is especially important in the finance and education sectors.
There are many different challenges in the development of Multi-Language NLP. For this reason, many different approaches have been adopted over time for the development of technology. We will discuss some of these approaches and models in detail.
One of the less successful models or approaches is to write algorithms that are trained on each language separately. This proved to be very costly and time-consuming. Given the low success rate, this approach has fallen behind.
There are some examples of this approach, for example, there are some models that are trained in German only. The downside of this endeavor is that it is extremely difficult. Many companies need models that can recognize several languages at once, and training models for each language individually would cost millions of dollars and take months.
NLP is based on analyzing a lot of data. Machines can analyze huge amounts of text at the same time and recognize languages and their vector fields. We will discuss some of the multilingual NLP models in detail.
We can take a brief look at how Non-English pipelines are formed to better understand these models. First, large amounts of data need to be collected. This data will then be labeled. A large amount of cleaning may be required within the data. The more resources available in the language, the easier it will be to prepare these pipelines. In this way, models can be trained more accurately and faster.
Multilingual NLP Tasks
It is very important to mention Multilingual NLP Tasks. There is still a digital perception that English is the language that everyone knows, that it is innate, and that it is used worldwide. This idea leads to social inequalities and these social inequalities are reflected in the future, in the technological world.
First of all, when machines can analyze a language, they encode and decode not only the linguistic structure but also the culture that this language is connected. Therefore, this process, which appears to be purely technological, becomes more cultural and socialized to a great extent. Problems such as racism, sexism, and discrimination in algorithms stem from such one-sided approaches.
On the other hand, the world is far from being a place where nations live in isolation, it is very global. The whole world interacts with each other. This increases the need for multilingual NLP and this technology is actively used in many different fields. For example:
· Recruitment processes and analyzing resumes in different languages
· Finance, review, and analysis of financial records in different languages, credit processes
· Security, analysis of criminal records and bureaucratic documents in different languages
· Education, equal opportunities, and analysis of transcripts or texts in different languages
· Fast and secure translation services
Many more important aspects and needs, such as the use of multilingual NLP can be given as examples of Multilingual NLP tasks.
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Cross-Lingual Language Models in Machine Learning
We mentioned that developing Cross-Lingual Language models is a very difficult task. We also talked about the fact that developing models for each language individually is a time-consuming, expensive and time-consuming process that has been tried before and has a low success rate. So which models are currently enabling machine learning technologies that can detect different languages?
Many different models make this possible. Different approaches and diversity in the field increase the chances of success. But some models stand out because they are more popular or more successful than others. The most important factor in the popularity of these models is their ease of use. Models that can be developed without the need for too much time and financial resources are of course preferred by many people. Let's take a brief look at some of these popular models.
mBbert
Bert, or Bidirectional Encoder Representations from Transformers, is a very important model. This model works unsupervised with pre-trained data. In other words, none of the large amounts of data used to train the model is labeled. In other words, it has not been human-controlled. In this way, the model can learn and evolve on its own in deep layers.
It can work in more than 100 languages. So what makes it possible?
· Masked Language Modeling (MLM): Here, the model randomly masks 15% of the words in the sentence or text. And the sentence has to guess these words. This distinguishes it from other ways of working, such as RNN, because it does not learn words one after the other.
· Next Sentence Prediction (NSP): Here the model combines two masked sentences as input. These sentences may or may not be ordered in the text. The model then needs to predict whether these sentences follow one after the other.
Through them, the model gains insight into how languages work. It can perceive languages without human supervision.
XLM
XLM is a model that combines many different models. These are:
· Casual Language Modeling (CLM)
· Just like Bert, a Masked Language Modeling (MLM)
· Translation Language Modeling (TLM)
XLM uses two different pre-training methods. These can be divided into supervised and unsupervised. One is the source language and the other is the target language. XLM has many different checkpoints where it checks whether the correct choice has been made.
Multifit
Muliti-fit is a model that works differently than the other two models. It is based on the tokenization of subwords, not words, and uses QRNN models.
Let's briefly explain subword tokenization. Morphology is the study of the structure, inflection, and inflection of words. Therefore, working only on "words" does not give accurate results in languages rich in this respect.
In morphologically rich languages such as Turkish, it is necessary to focus on subwords to get accurate language perception results. Because the inflections of words are very common in Turkish, these sublimes are also quite numerous.
The tokenization of these subwords allows the machines to detect words that are not very common.
Bottom Line
The globalization of technology creates the need for technology to work in many languages. Here, making technology work does not only mean offering language options.
It also means the need for human-machine communication to be carried out in different languages and for machines to be able to perceive languages used around the world.
These Cross-Lingual Language models offer effective solutions to our problem. The ability of machines to recognize languages other than English in areas such as translation and text analysis is based on these models.
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For more information on NLP and text analysis, please visit the content below:
