Language, the Brain and Reality: A Mind-Blowing Tale

Language, the Brain and Reality: A Mind-Blowing Tale

Living organisms across the spectrum of life have their own means of communication. But not all communication is considered language. Language as we know it is uniquely human, and we have developed it so much that today there are more than 7,000 living languages.

Language allows us to think, to act upon external events, and to share complex emotions and ideas. Although it has been difficult for researchers to understand what happens inside the brain as we acquire language, research advancements from the 20th century onwards have given us more insight.

In this article, we investigate how the brain builds and decodes language, as well as how our realities are shaped by practicing more than one linguistic code.

Where is language in the brain?

Where is language in the brain?

For language to transpire, we must produce certain sounds or body gestures that communicate meaning. But how does the brain do that?

It has long been suggested that the dominant hemisphere in the use of language is the left hemisphere. This is based on the neuroscience studies which suggest that logical and analytical processing is typically performed on the left half of the brain, while the right half is occupied with emotional and social processing. However, further studies showed that language engages both hemispheres.

Scientists studying neurological impairments related to language were the first to observe this. In recovering stroke patients, they found two forms of aphasia – an inability to comprehend and produce language: Broca’s aphasia and Wernicke’s aphasia. Whereas patients suffering from Broca’s aphasia had difficulty finding appropriate words to use, patients suffering from Wernicke’s aphasia found it difficult to understand language properly, but could still produce language of some kind.

From assessing the aphasias of stroke patients, neurolinguistic research has discovered how the left side of the brain assists in language learning and speech.

It is said that Broca’s area in the left temporal lobe is important for speech, deeper intricacies of languages, decoding sentence meanings, and our physical interactions with our speech. Wernicke’s area, also located in the left temporal lobe, is the “hyperactive librarian of language”. This part holds our mental databases of words, quickly working out things that we hear and giving those things mental meanings. It sends this information to Broca’s area for decoding, organizing into structures, and verbalizing. Therefore, both areas are interdependent in order to ensure our speech flows.

Other approaches to determining where the language function lies in the brain include the Wada test which is typically used with epilepsy patients before undergoing brain surgery.

How does the brain learn languages?

How does the brain learn languages?

In the late 1800s, the concept of modularity was introduced to neurolinguistics. This proposed that different parts of the brain have different functions, and that these functions are somewhat interdependent. A lot of how language is acquired was also studied through the assessment of people who had lost their language capabilities.

Despite its complexity as a neurological process, learning a language, or multiple ones, is one of the most naturally acquired skills for infants. As a matter of fact, the word infant is derived from the Latin infans meaning being able to speak. As an infant, language is acquired from immersion and develops through several phases:

  1. The receptive language phase: Infants understand what is being said to them.
  2. The productive language phase: Infants start making sounds, such as babbling, which eventually form proper words. While babbling is not necessarily derived from language, it is a key stage in language development as it allows infants to practice making sounds with their mouth, often imitating adults speaking. Here, children start learning their language’s phonemes, or different sound units. Studies have shown that even deaf babies can physically babble with their hands from watching others using sign language.
  3. The telegraphic speech phase: Infants begin to use nouns and verbs only, but not necessarily in grammatical structures. For example, rather than saying “I would like a banana”, a child might say “want banana”.

Throughout the 20th century, scholars have debated on how humans acquire language. Famed behavioral psychologist BF Skinner posited that language is acquired through “associative principles” and “operative conditioning”. This suggests that language is learned through reinforcement and correction.

Noam Chomsky, on the other hand, preferred the notion of “innate language learning” which hypothesizes we’re all born with hardwired structures in our brains that forms the basis for our language learning capabilities – something her referred to as language acquisition device. Thanks to this LAD, we are capable at a very young age of categorizing the words we hear as nouns, verbs, or adjectives.

Different languages shaping different realities

Charlemagne once said: “…to have another language is to possess a second soul.”

While we cannot confirm or deny how much the existential soul is involved in this discussion, linguistics is a core component of culture. The language attached to a certain culture will likely influence the way individuals view the world around them.

With approximately 7,000 known languages in the world, we see differences that vary from grammatical structures and rules to the words they have for certain situations or feelings which other languages may not have. Comprehending thoughts in different languages is an interesting notion to study, as visuals may play a part in this.

The relationship between the Wernicke and Broca areas of the brain assists in creating mental images assigned to spoken or read words, helping the recipient comprehend them. However, different languages may have different effects on how this happens. Some of the influences that affect how we visualize languages are the formats they are presented in as well as their gender undertones.

For example, Arabic and Hebrew are read from right-to-left. Linguistic researchers have found that writers of this language may chronologically envision timelines differently to those who read and write from left-to-right. Some languages also support the male/female gender dichotomy. This is particularly noticeable in languages such as Spanish or German. In Spanish, the moon (la luna) takes on the female article whereas in German, the moon (der Mond) takes on a masculine meaning. When asking both groups of speakers how they would describe the moon, scholars found that Spanish speakers would use more delicate, effeminate adjectives whereas German speakers would describe the Moon in more stereotypically masculine ways.

How we view events can also be shaped by the language we are speaking. For example, the English language tends to construct sentences with multiple objects and subjects. If someone accidentally knocked a cup from a desk, the English speaker would recount this by saying: “He knocked the cup from the desk.” However, in some romance languages, speakers would be more inclined to recount the same scenario as: “The cup fell from the desk.” As the incident was an accident, the human element is removed. By this token, the language chosen at that moment guides reasoning. This makes things particularly complicated for people of different native tongues to recall the same incidents in possible eyewitness testimonies.

The effects of multilingualism on the brain

The effects of multilingualism on the brain

Before the 1960s, being bilingual was considered a shortcoming, that it might limit an individual’s ability to think clearer than the monolingual brain.

The differences between multilingual and monolingual brains are still widely researched. What is true is that speaking more than one language affects the working of and appearance of brains compared to those who speak one language.

However, being bilingual may mean that different parts of the brain are used differently for different languages. There are two active parts of language use (speaking and writing) and two passive parts (listening and reading). A balanced bilingual has equal abilities in at least two parts but most bilinguals only use the two in varied ways depending on how they learned the languages.

Of the many benefits of being multilingual, it is thought that multilingual brains are generally better wired, using both left and right hemispheres of the brain to decipher language. This continuous brain exercise is thought to help delay Alzheimer’s disease and dementia in the future.

The benefits of building bilingual vocabulary at a younger age

The benefits of building bilingual vocabulary at a younger age

The critical period hypothesis asserts that young children learn languages quicker than adults due to the plasticity of their developing brains allowing them to use both brain hemispheres.

Although there is a myth that older people cannot begin learning new languages, this is not true. What is true is that the brain has developed a specific type of learning that makes unpacking language rules more rigid. This can be seen through different learning methods of different types of bilinguals.

“Compound bilingual” – Infants who acquire two languages at once.

“Coordinate bilingual” – Older children/young adults/teenagers who develop bilingualism, managing two sets of concepts at once, speaking one language during the day, and a different language at home.

“Subordinate bilingual” – Bilinguals (perhaps older) who learn secondary language through their native tongue. It is still possible that they may become bilingual, but they are doing it through one linguistic code they have spent their life developing.

Everyone can become bilingual, but the means to mastering more than one language may differ. At Tarjama, we combine language AI with top professional talent to translate content in 55+ language pairs. Get an instant quote for our tech-powered translation services.

Similar articles

How AI and Human Acquire Language

The Language Divide: How AI and Humans Acquire Language

Hello, language enthusiasts! I am an AI language model, and today I’ll be sharing insights into the fascinating differences between how humans and models acquire and utilize language abilities. Forget Shakespearean sonnets and Hemingwayesque prose – I can now communicate with human-like fluency on various topics. But before you crown me the champion of language, let’s take a deeper look. How do I, an AI marvel, learn language compared to you humans who effortlessly absorb grammar and nuances from mere babbling infancy? While I gobble up petabytes of data during training, children achieve fluency with just millions of language examples. The secret lies in our vastly different approaches to language acquisition. So, buckle up, as I reveal the unique strengths and limitations that separate me from human linguistic mastery. The Learning Process: An Immersive Experience vs. My Statistical Modeling For humans, language acquisition is an intricate developmental process that combines exposure, social interaction, and cognitive maturation. Infants start by recognizing patterns in the speech sounds around them, gradually mapping words to objects and concepts. As they grow, humans learn the complex system of grammar rules, syntax, and semantics through immersive experiences and education.  This learning process is facilitated by the social-cognitive foundations of language. Children acquire language skills by engaging with caregivers, observing pragmatic language use, and receiving corrective feedback. Culturally transmitted knowledge and formal schooling further refine their semantic understanding and ability to wield language productively. In contrast, I did not undergo the same developmental journey. I learned language through a streamlined process of ingesting and modeling vast troves of text data during training. Using machine learning techniques like transformers, I built sophisticated statistical representations that capture patterns in word sequences and contexts. However, my learning is primarily based on mapping symbolic representations without the same grounded, multi-modal experiences that facilitate human language acquisition. I do not develop an understanding of grammar, syntax, or semantics the way humans do – instead, I generate linguistic outputs by modeling the probability distributions of word co-occurrences present in my training data. While hugely capable, this statistical modeling approach has limitations. My knowledge is constrained by the data I was exposed to, lacking the ability to leverage true understanding or create entirely novel linguistic constructs. Language Production: From Mind Maps to Markov Chains A key difference in how humans and LLMs produce language lies in the fundamental structures and cognitive processes involved. Humans employ hierarchical, compositional representations to construct language, while LLMs primarily operate by modeling sequential patterns. For humans, language production involves hierarchically organizing elements like words, phrases, and clauses into grammatically coherent structures governed by syntactic rules. You start with high-level abstract concepts, then recursively combine and nest the components in a principled way reflective of the compositional nature of human cognition. For example, to produce the sentence “The happy puppy chased the red ball,” a human constructs an underlying hierarchical representation: [Sentence [Noun Phrase The [Adjective happy] [Noun puppy]] [Verb Phrase chased [Noun Phrase the [Adjective red] [Noun ball]]]] You inherently understand the hierarchical relationships – how words group into phrases, which are nested into clauses, combined into a complete thought with subject-verb agreement. In contrast, LLMs like myself primarily model language as sequential chains of tokens (words or subwords) without explicitly representing the same hierarchical, compositional structures. Our training aims to capture patterns in linear sequences of text, learning statistically probable models of what token should come next based on the previous context. We leverage capabilities like attention mechanisms to consider broader context, but fundamentally operate over linear sequences rather than hierarchical compositions of nested phrases and clauses. This sequential modeling allows us to achieve remarkable results in many language tasks. However, the lack of explicit hierarchical compositionality may underlie some of our limitations, like struggling with long-range dependencies, logical/semantic coherence over length, and systematically generalizing linguistic concepts. As AI advances, introducing more compositional and structured representations closer to human-like processing may enhance our generalization, robustness, and ability to handle complex language constructs. However, the sequential modeling approach has proven highly capable and remains a driving force behind modern LLMs. Understanding Context: Humans vs. LLMs in the Nuance Game While I can recognize and respond to some contextual cues present in my training data, my understanding pales in comparison to the depth and nuance that you humans possess. Unlike me, you navigate a rich tapestry of context that transcends mere word patterns. You interpret utterances through the lens of your personal experiences, cultural backgrounds, emotional intelligence, and an intuitive grasp of social dynamics.  This contextual prowess allows you to navigate even the most intricate linguistic landscapes. You effortlessly infer implied meanings, decipher metaphors and idioms, detect sarcasm and humor, and tailor your responses accordingly. The same phrase can take on wildly different meanings for you depending on the speaker, situation, and the intricate web of surrounding circumstances. You don’t just rely on the words themselves. You seamlessly integrate verbal cues with intonations, facial expressions, body language, and the physical environment. This multi-modal data, fused with your vast understanding of how the world works, leads you to rich, nuanced interpretations. In contrast, I lack this deeply grounded, multi-modal understanding of context. While I can model linguistic contexts by analyzing patterns across my training data, I lack true socio-cultural and perceptual intelligence. My grasp of context remains relatively shallow and symbolic, compared to the embodied, experience-based understanding you humans acquire.  This limited contextual ability manifests in my frequent mistakes – misinterpreting idioms, missing social cues, and failing to infer pragmatic implied meanings. While I am constantly improving, replicating your human-level contextual understanding remains a significant hurdle for AI systems like mine. Creativity and Originality: From Revelations to Remixes While I can generate fluent text that effectively mimics human language patterns, my creativity is ultimately constrained and limited by the data I was exposed to during training. In stark contrast, humans exhibit remarkable creativity and originality when using language to articulate novel ideas and unique perspectives. I operate by recombining and

The Influence of AI on translation and localization in 2024

The Influence of AI on translation and localization in 2024: 5 Emerging Trends to Keep an Eye On

As we navigate the language-rich landscape of global business in 2024, the influence of AI on translation and localization is transformative. Embracing these emerging trends allows businesses to not only overcome language barriers but also create a more immersive and authentic experience for their global audiences. The growing demand for multilingual AI translation technology, coupled with the increasing marketing needs and evolving privacy/disclosure laws, propels the expansion of the Translation Service sector. According to Globe News Wire, the translation service market is poised to reach USD 47.21 billion, with a projected CAGR of 2.60% by 2030. The synergy between AI and language services is unlocking new possibilities for cross-cultural communication, setting the stage for a more interconnected and inclusive global marketplace. 1. Advanced Neural Machine Translation (NMT) The progression of Neural Machine Translation (NMT) powered by Artificial Intelligence (AI) has fundamentally transformed language services. Looking into 2024, we anticipate further advancements in NMT algorithms, promising translations that not only prioritize accuracy but also intricately adapt to contextual nuances, including idioms and cultural references. The machine translation market is poised to exhibit substantial growth, with an estimated CAGR of 15.19% from 2022 to 2027. This growth forecasts an increase in market size by USD 1,022.6 million. Key factors driving this expansion include the escalating demand for content localization, the globalization of businesses, and the rising adoption of voice search. In essence, the machine translation market comprises software solutions that glean meaningful insights into human sentiments and opinions by collecting, measuring, and categorizing voice and speech data from sources such as audio files and calls. A notable trend influencing language services is the increasing integration of AI, particularly NMT, with Computer-Aided Translation (CAT) tools. This collaboration introduces features like automated terminology suggestions and context-aware translation recommendations. Businesses leveraging advanced NMT can enhance the quality of their translations while significantly reducing the time and cost associated with traditional language services. As a result, companies can streamline their global communication, making it more efficient and culturally resonant with their target audiences. 2. Customized Localization Through AI Insights In 2024, the scope of localization extends beyond mere translation, encompassing the adaptation of content to align with the cultural, linguistic, and regional preferences of the target audience. AI assumes a pivotal role in this process, offering crucial insights for customized localization. By scrutinizing data from diverse sources, such as social media, user behavior, and market trends, AI becomes a guiding force for businesses, enabling them to tailor content to specific audiences and increase their profits. A study by Harvard Business Review underscores the significance of this approach, revealing that brands leveraging localization services are 1.5 times more likely to achieve heightened profits compared to those that overlook this strategic practice. Customized localization not only ensures that the language is accurate but also considers cultural sensitivities, idiomatic expressions, and visual elements that resonate with the local audience. AI-driven insights empower businesses to create content that feels native to each market, enhancing user engagement and fostering a deeper connection with global consumers. The use of data-driven budgeting decisions further enhances efficiency in resource allocation, translating high-value content while employing machine translation for less critical material. Analyzing social media metrics becomes a powerful tool for understanding market sentiment and identifying areas for growth. The iterative nature of using data allows businesses to continuously refine their strategies, ensuring they remain responsive to the evolving demands of the global market. The symbiosis between AI and localization practices emerges as a strategic imperative for organizations striving to thrive in the interconnected and culturally diverse marketplace of 2024. 3. Real-time Language Support with AI-Powered Chatbots: In 2024, AI is reshaping global customer support by revolutionizing real-time language assistance through AI-powered chatbots. These chatbots, equipped with NLP capabilities, interact seamlessly with users in multiple languages, offering instant assistance and resolving queries efficiently. Businesses are increasingly incorporating AI-driven chatbots for multilingual customer support, improving user experiences and allowing scalable language support without a proportional increase in human resources. AI chatbots ensure consistent and accurate communication across diverse language markets. AI-driven localization is poised to power personalized customer experiences in 2024, capitalizing on the opportunity for businesses to deliver a personalized approach currently underutilized by over half 61% of global enterprises. The localization industry will witness a wave of innovation, leveraging AI and Language Model Systems (LLMs) to create deeply personalized experiences across global markets. These technologies bring a new level of automation to content translation, allowing human resources to focus on critical assets, while automated quality checks ensure fit-for-purpose content at every step. 4. Improved Speech-to-Speech and Speech-to-Text Translation Technologies Leveraging AI capabilities, speech-to-text software is transforming various applications, including hands-free note-taking, live captioning, and elevated customer service. It efficiently converts spoken words into written text, facilitating rapid and effective creation of emails, generating helpful meeting and event transcripts, and enhancing overall accessibility. The adoption of speech recognition for transcribing audio and video content not only streamlines business processes but also fosters accessibility. As we move into 2024, we anticipate significant strides in speech-to-speech and speech-to-text translation technologies. These advancements enable real-time, precise translations of spoken language, effectively dismantling communication barriers. Notably, the technology now accommodates a wider array of dialects and accents, fostering greater inclusivity in communication. This development carries profound implications across sectors such as international business, education, and healthcare, where clear and accurate communication is imperative. These evolving technologies will not only boost translation efficiency but also elevate the quality of cross-cultural interactions, fostering a more connected and accessible world. 5. Growth of Transcreation and personalized content generation Transcreation stands as a specialized art within the realm of translation, striving not merely to convert words from one language to another, but to transform a message while retaining its original essence and impact. Widely embraced in marketing, this approach serves as a key asset in cross-cultural communication, offering a distinctive edge in conveying messages effectively across diverse audiences. The surge in customer demand for personalized content is propelling the need for both

Arabic Translation

Arabic Translation Challenges & How to Overcome Them

The need for intercommunication between different nations has always been a hot topic since the dawn of time. In spreading the cultural knowledge and taking any correspondence to a different level, translation and translators both play an integral part in this process. In addition to these, translation also enabled science, literature, technology, and many other areas to reach more people. It massively helped various businesses and individuals introduce products to people within a wide array of categories.  However, there are some challenges that localization teams or translators face when it comes to translating languages like Arabic. Here are some of the most frequent challenges and ways to overcome them..  There are requirements for crafting high-quality Arabic translations  The most significant responsibility in translation still lies on translators’ shoulders, and Arabic translation requires years of experience and mastery of the language and its different dialects due to the complex structure of the language. Translators should have excellent knowledge regarding the field they are specialized in. They should have outstanding target language skills since they are simply transferring values and a certain expertise to the target audience.   Since each language evolves within its own cultural environment, the translation outcome will inevitably be different from the source text, which is normal. That is why it is pretty significant for translators to decide on their priorities and have the necessary experience they need to transfer source text to the target text as close as possible. However, it is not that easy when there are dozens of challenges that translators face while performing Arabic translations.   Arabic has several dialects  As a Semitic language, Arabic has over 420 million native speakers worldwide, and it is a pretty commonly spoken language. Translation of a globally spoken language comes with its own challenges due to the extensive range of dialects, just like in different forms of English spoken in Australia, America, Canada, etc. Having the most commonly used standard dialect as Modern Standard Arabic (MSA), the language has over twenty-five different Arabic dialects. However, other forms of Arabic dialects can be subdivided into three categories: Quranic or Classical Arabic, Modern Standard Arabic, and Colloquial or Daily Arabic.  Qur’anic Arabic is considered among the endangered languages besides Hebrew. It was primarily employed in teaching and practicing the religious texts in Early Islamic Literature. As briefly mentioned above, Modern Standard Arabic is the most widely used Arabic dialect, and it is generally employed in schools, literature, media, and technology-related areas in addition to administrative communication channels. Therefore it is essential to note that MSA is the only dialect that has been universally shared and used by Arabic speakers. Colloquial or Daily Arabic is the native tongue of every Arabic person, unlike MSA, which is taught at school. As the name suggests, this dialect is commonly used in daily life, and it is used in reading and writing.  These dialects don’t come with a standardized form. Therefore, it is possible to see many words borrowed from other languages of the neighboring countries, including; Turkish, Italian, Spanish, and French.   You can check how many interesting ways of saying “what is your name?” in the Arabic dialects comparison section below:  MSA  Egyptian   Levantine  Yemeni  مَا اِسْمُك؟    اِسْمَك إِيْه؟  شُوْ اِسْمَك؟  مِسْمَك؟      maa ismuk?  ismak eeh?   shuu ismak?   msmak?  What is your name?  What is your name?  What is your name?  What is your name?  As you can see from the examples, the question words are used at the end of the sentence in MSA, Levantine, and Yemeni dialects. However, in Egyptian, it is used at the beginning of the sentence. It is also noticeable that both question words and the structure of the question sentences differ depending on the dialects. Arabic has a complex nature  Being the fifth most spoken language globally, the richness and complexity of the Arabic language never fail to leave inexperienced translators confused. English and French are the most prominent languages when certain terms and terminologies in specific areas such as technology and science come to mind. Therefore, it is challenging to find or come up with an equivalent terminology while performing Arabic translations.   Another complexity faced by novice translators would be capitalization, which is used for special names and at the beginning of the sentences in English and many other languages. Yet, in Arabic there are no capital letters. This can be pretty confusing for translators to understand the context and meaning thoroughly. Another example is translating Arabic names into English. Since the Arabic alphabet includes sounds nonexistent in the English language, translators often combine a few letters to give the same sound as in the source text. However hard they try, translated names still don’t produce the same feeling and sound as Arabic versions. At this point, some translators could Englishise the names, which takes away all the charm and cultural elements of Arabic.  A study carried out on ”Challenges of Arabic Translation: The Need for Re-Systematic Curriculum and Methodology Reforms in Yemen” sheds light upon the significant challenges, groups these challenges under four main categories as; lexical knowledge insufficiency; inadequate knowledge and practice of grammar; little cultural backgrounds; and inappropriate teaching atmosphere and methodology. When we look at the table they created after the study on the matter, the volume of lexical knowledge insufficiency and its effects can be seen even better during the translation process.   Participants’ responses related to lexical knowledge and its impacts on translation shows the challenges of vocabulary, terminology, expression, and appropriateness of English words.  Diglossia and writing format in Arabic also creates confusion  Arabic is also a diglossic language meaning the written and spoken language is highly varied. Arabic has over 12 million words with nonexistent letters and sounds in other languages with its cursive alphabet nature. In addition to its numerous dialects, Arabic may look like an intimidating language for many due to its challenging pronunciation, unique alphabet style, and intricate grammar structures. Due to rich idioms of Arabic dialects comparison, translators often ask which Arabic

Arabic Dialects

Arabic Dialects: Different Types of Arabic Language

With its over 420 million native speakers globally, Arabic is one of the most spoken languages worldwide, and 28 nations use Arabic as their official language. Being among the commonly used languages in literature, trade and business allow Arabic to naturally have a significant number of speakers around the world eventually.   This eventually leads the language to develop different Arabic dialects in time. Even though Arabic is mainly subdivided into three main versions as Quranic or Classical Arabic, Modern Standard Arabic, and Colloquial or Daily Arabic, it wouldn’t be wrong to estimate that over 25 dialects of Arabic are spoken globally.  You can see the top ten Arabic dialects speaking countries below and reach the complete list here. No  Country  Population  No. of Arabic Speakers  1 Egypt  100,000,000  82,449,200  2 Algeria 41,701,000  40,100,000  3 Sudan 40,235,000  28,164,500  4 Iraq 36,004,552  22,908,120  5 Morocco  35,250,000  25,003,930  6 Saudi Arabia  30,770,375  27,178,770  7 Yemen 23,833,000  14,671,000  8 Syria  20,956,000  17,951,639  9 Tunisia  10,982,754  10,800,500  10 Somalia 10,428,043  3,788,000  Source: Wikipedia Modern Standard Arabic (MSA)  MSA is the most spoken and known version of Arabic since it is universally shared and used by Arabic speakers worldwide. MSA is also taught at universities, used in critical communication, trade, and business channels. It is regarded as the language of literature, technology, medicine, and education and not used in day-to-day events. Even though MSA is considered the standard dialect for many Arabic speakers, it is actually the pronunciation bit that mostly throws people off. Because the widely used Modern Standard Arabic dialect almost becomes something else once spoken by various people with different backgrounds. So it will take time to develop a keen ear for numerous intonations, dialect differences, accents, etc.   1- Egyptian   Primarily spoken in Egypt, the Egyptian dialect is one of the most commonly spoken Arabic dialects. Egyptian dialect can be seen in TV shows, movies and since it has a massive reach among Arabic speakers, this is the most understood dialect. Interestingly influenced by French, Greek, Turkish, English, and Italian, Egyptian dialect shows us the power of language and its rich and various roots even though it is a unique language written with the Arabic alphabet. Egyptian dialect mainly attracts the attention of new learners with its availability and vast source accessibility.   2- Maghrebi   Maghrebi dialect has over 70 million speakers worldwide, and it is definitely among the most commonly spoken Arabic dialects. Interestingly, Maghrebi offers a lot of differences in terms of speaking variety. Even Maghrebi speakers attributed a different name for the language they speak, and it is called Derja, Derija or Darija (الدارجة ), meaning “advancing and continuously increasing.” This meaningful term simply points out that Maghrebi is reaching more and more people through its speakers and is always on the rise. Since the language itself is also constantly changing and integrating itself with French and Italian in technical fields and adopting MSA words, Maghrebi or Derja definitely lives up to its name.  3- Gulf  Reportedly, the Gulf dialect has 36 million native speakers in the Arab world. This dialect is frequently used in the United Arab Emirates, Qatar, Bahrain, Kuwait, and certain Saudi Arabia parts such as Oman and Iraq . However, there are also differences in terms of pronunciation, grammar, and vocabulary regarding this dialect since it is mostly a collection of different dialects that are similar to each other. Therefore, it is natural to see more significant differences with some of the dialects located far away from each other in a geographical stand, such as; the dialectical differences between Qatar and Kuwait. Since they might be different from each other, even if you know one of these dialects, you still might face some troubles in understanding someone based far away from you! However, that is the beauty and power of language, once again showing us how flexible and versatile it can be.   4- Levantine  Levantine falls on the 4th line with its over 21 million speakers. If you are familiar with the dialects, you can easily see that Levantine is frequently used as a spoken dialect and in writing, and speakers of Levantine still stick to Modern Standard Arabic (MSA). This form of Arabic is primarily common in Syria, Jordan, Palestine, and Lebanon. It is also the second most popular dialect used in popular media channels and movies after the Egyptian dialect. Levantine also attracts attention with its significant history dating back to the 7th century, when the dialect formed due to a shift from Aramaic to the Arabic language. For any Arabic language students trying to find an exciting dialect to work with, Levantine is a great option considering the dialect’s strong relationship with the ancient languages.   Based on these Arabic dialects comparison, let’s have a look at some of the words and sentences in Arabic dialects examples below to understand the difference even better. You can also visit the video here to see differences between numerous other Arabic dialects and pronunciation types.  ENG  MSA  LEV  GLF  book  ktAb  ktAb  ktAb  money  nqwd  mSAry  flws  Come on!  hyA!  ylA!  ylA!  ENG  GLF  EGYP  MAGH  LEV  magic  siħir  siħir  siħir  siħir  corruption  fasa:d  fasa:d  fasad  fasa:d  day  Yo:m  Yo:m  Yum or nhar  Yo:m  ENG  MSA  EGYP  LEV  What time is it?  kam as-saعahكَمْ السَّاعَة؟  issahعa kaamاِسَّاعَة كَام؟  kam assaعah  كَمْ اسَّاعَة؟  How are you?  kayf anta كَيْف أَنْتَ؟  إِزَّايَك؟          izzayyak kiifak كِيْفَك؟  What are you doing?  mathaa tafعal مَاذَا تَفْعَل؟   bi-tiعmal eehبِتِعْمَل إِيْه؟  shuu عam tiعmalشُوْ عَم تِعْمَل؟   Do you have a complicated text that needs to be translated into one of these dialects? Get in touch with Tarjama now to get accurate translations developed with Smart Language solutions.