The application of humongous data sets available via ever-expanding text, digital images, videos, and deep learning models make it possible to train computers to interpret and understand the visual world in ways that are similar to how humans do. Deep learning and machine learning are changing the way we interact with technology because of the advancements in deep learning that allows machines to understand natural language with a high degree of accuracy.

Creating large data sets with the proper context; is where data annotation and labeling step in. The computer vision model’s accuracy is determined by the quality of these annotations and labeling, which are used beyond the classification of objects. The annotation process includes providing feedback to the machine learning algorithm that trains it so that the system can improve its prediction capabilities.

Broad understanding of why text annotation or image annotation is used.

Both text and image annotations are used to enrich the information on a given topic or data set. This allows for an interactive experience and provides the viewer with additional details.

With text and image annotations, one can discern important information such as the location of a specific landmark in a photograph, highlight key moments in a video or find the intent of a public release.

Technology can be used as a platform for the efficient processing of information in different ways, such as through text annotation or image annotation. When text annotation or image annotation is done on public data sets with AI/ML models, feedback also helps improve AI/ML models.

In short, both text annotation and image annotation, along with video annotation, are a subset of data annotation and labelling and are used in improving the context, intent, and sentiment of information (which can be text, image or a video) with the help of AI/ML tools.

Text Annotation Explained

Text annotation is a process where text is annotated with tags or other metadata. An annotation might look like highlighting information, marking a text with symbols to represent ideas, or writing summaries at the end of a chapter or section for an easy review.

The annotation can be done automatically by using optical character recognition (OCR) and natural language processing (NLP) or manually by a trained human.

Text annotations can be used to describe the content of documents, categorize them or find specific information in them.

Annotation tools are often used in libraries and archives to help organize and find resources. They are also used in the publishing industry, providing valuable information about the publication of books, journals and articles.

In addition, annotation tools may help mark-up web pages with metadata such as keywords and page rank.

Statista shows that the global NLP market will generate revenues of over $12 billion in 2023-24. The market is predicted to grow by 25% per year, which will result in revenues of over $43 billion by 2025. Text annotations are an essential part of the NLP development procedure. It is fair to consider text annotations as an important phenomenon.

Types: Text annotation can be of different types based on different data set and use case.

a. Entity Annotation & Entity Linking: Entity annotation is one of the most critical tasks in NL processes. It is essentially the process of locating, extracting and tagging entities in text. On the other hand, entity linking involves connecting your entities to more extensive data repositories.

b. Text Classification: Text classification tasks involve annotators reading through a body of text and classifying its overarching subject, intent, and sentiment. Text classification can be used for various reasons, such as telling the difference between when food allergies are mentioned versus allergies to specific things unrelated to food. Text classification is also known as document classification and text categorization.

c. Sentiment Annotation: More broadly called sentiment analysis or opinion mining, sentiment annotation is the process of labelling emotion, opinion, or sentiment within a piece of text.

d. Linguistic Annotation: With linguistic annotation, annotators are either identifying or flagging grammatical, semantic or phonetic pieces of text. Linguistic annotation is a crucial component of the more general label text analysis, which includes annotating other textual features such as punctuation, tone and word length. Annotating linguistic text includes identifying words or phrases that have unclear meaning due to the use of cultural, regional or other language-specific words.

Image Annotation Explained

Image Annotation is the process of adding labels to images. In a simple case, it’s like giving human annotators images of different cars and asking them to label each image with its colour, model name and country of origin.

Annotation can be done manually or with the help of machine learning. The accuracy of image annotation is improved by using a model trained on annotated images from a large data set. The training process involves labelling different parts of an image and assigning them semantic labels.

With the help of image annotation, it is possible to scale up workflows for image processing.

Computer vision (a field of AI, ML) continues to be an ever-increasing part of our lives. It’s gone from healthcare, automotive and marketing to even online dating. According to Forbes, the computer vision market is predicted to be worth $50 billion in 2022.

Types: Image annotation can be of different types based on what we use it on.

a. Bounding Boxes: A frame is drawn around the object to be identified. You can draw a bounding box around 2D or 3D images.

b. Landmarking: It is a technique that identifies facial features, gestures, expressions and emotions. It’s also used for marking body position & orientation.

c. Masking: These are pixel-level annotations that hide some portion of an image and make other areas more visible. The primary use for this is to focus on specific parts of the image or highlight what you want people to notice.

d. Polygon: This technique is used to identify the pick point of the object in question and its edges: Useful case of polygon labelling is to identify objects and items with irregular shapes.

e. Polyline: This model helps create ML models for computer vision and autonomous vehicles, and it helps to recognize directions, signs, turns, and incoming stream of vehicles to make sense of their environment and be safe when driving.

The Advantages of Text Annotations over Image Annotations

Text annotations are more versatile than image annotations. Text annotation can be used in many different formats, and they are searchable. Images are not searchable and don’t have the same level of versatility as text annotations.

Text annotations are a form of visual representation of the text on a platform. They can be created by highlighting, underlining, circling or adding comments, and it is up to the user to decide what information gets displayed in these annotations. Some advantages of text annotations include making better reading comprehension and highlighting keywords.

The Disadvantages of Text Annotations over Image Annotations

Text annotations are often used in educational settings to provide additional information to the reader. However, text annotations have some disadvantages that make them less desirable than image annotations.

Text Annotations: Text annotations can be challenging to create and maintain, especially if they need to be updated regularly. They also require more screen space than image annotations, making it difficult for students with visual impairments or learning disabilities to use them.

Image Annotations: They are easier for students with visual impairments or learning disabilities because they don’t require as much screen space and can be created and maintained more easily.

Conclusion:

Text annotation is a relatively new research field still in its infancy. It is very different from image annotation, which has been researched for decades. Image recognition accuracy only recently improved, making image annotation more relevant in the past ~10 years.

Image annotation is a difficult task for humans and robots alike. It requires understanding the image’s content and then providing labels to help describe it. Text annotation, on the other hand, is more linguistically oriented. Its goal is to understand the content of a document, extract facts from it, and provide contextual information such as definitions or synonyms.

Text annotation uses natural language processing to annotate text with metadata information such as sentiment, topic, and other relevant information.

Image annotation can be done by hand, or it can be automated using computer vision techniques.

Most likely, in the coming time, text annotations will be used to annotate images and videos as well. We are already seeing promising results in AI-generated artworks where we can generate images by adding a few descriptive words as seed phrases.

Vendor of choice for your project

Ekit.AI provides the text annotation, text labelling & image annotation services for NLP in deep learning. We work with highly experienced annotators to identify such texts and label or annotate them per the custom requirements. High quality and the accuracy is maintained to make sure each vital text/image is annotated with suitable metadata to make sure it can produce the correct mix of labelling and annotation for different needs at the best pricing in the market.

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Centuries ago, businesses were less hectic than they are today, and thousands of entrepreneurs would willingly testify to that statement. Not only was the competition comparatively lesser than it is today, but customers knew what they were investing in – not to mention, better alternatives were hard to find.

However, in today’s market, a competitive marketing strategy is undoubtedly one of the most important factors that business owners need to focus on. If you are a small or even a large business owner and have revised your strategies, but to no avail, it is evident that you are doing something wrong.

Now, it is always important to understand that just because your strategy has not worked for you or given you the desired results, does not mean you failed. Be open to change.

Nevertheless, the real question remains – what will it take to extend your reach not only to the operational region but also farther than the very borders of the functional state.

Well, just a few tweaks here and there, to be honest, but it is essential to understand the impact of Multilingual Market Research in your marketing strategy. Yet sadly, it is not that simple.

Understanding Going Global

Primarily, it is crucial that you understand how difficult it can be to start functioning on a global level – especially if your business is relatively smaller in comparison to rivals. Many factors must be taken into consideration regarding Multilingual Market Research before the expansion of your business.

This is crucially important due to the mere fact that businesses need to be ready for the exposure that they might receive as well as be able to fulfill the demands of their new customers in the new location.

For this reason, among many others, the business owner must be willing and able to pay a substantial investment of not just budget, but also time. Many entrepreneurs have made the amateur mistake of assuming that their business will be functional on a global level only by installing an online website translator, which can lure in customers from different parts of the world.

Needless to say, it did not take very long for them to realize that things were not so simple.

Moving on, small business owners need to be as close to reality as possible, while working on numbers and calculations throughout their endeavor. One of the most salient facts that they must be accepting of is that you cannot expand to another country if your reputation at home is suffering?

What that means, you might wonder. It is not possible to expand to a different country or region and expect to make a huge profit if your consumers back home have nothing but complaints in regards to your products or services. To expand positively, the local district must be pleased.

Once you have cleaned up your slate back home, there is no reason why you should not be going global. It is recommended, however, that you ensure your business is ready for global expansion.

Understanding the Challenges of Going Global

As aforementioned, expanding on a global scale is difficult – but certainly possible. However, it is essential to understand and be prepared to devise a marketing strategy that would work following the goals of your company. Of course, it is crucial to incorporate Multilingual Market Research into your marketing strategy. Here are some challenges that you should consider:

An Unknown Audience

When working in a new region, many new business owners tend to make the common mistake of treating their new audience like their old one. This, of course, is a rather unwise decision.

New regions, cities, or countries have their likes, dislikes, demands, etc. Therefore, to treat them the same as your home base would not be smart at all. Before expanding, it is advisable to conduct proper market research on the locality that you are expanding your company to.

This step will help you understand what is right and what is not for the people you will be catering to.

Localization is Key

As per the definition provided by Tech Target, localization is the adaptation of a product or service to meet the needs of a particular language, culture, or desired population’s “look-and-feel.”

As mentioned before, when expanding your business to a new region, it is crucial to ensure that your products and services are able to cater to the needs of your new potential consumers. However, failure to do so will certainly guarantee that your business will not go very far.

Thus, it is crucial to add localization in your marketing strategy so that you can ensure that your services are designed specifically with the needs, wants, or even the culture of your new customers.

Operative Transcreation

If the new region that you have selected speaks a different language, then you must first enable and make sure that you can communicate effectively. This is known as transcreation. This also means that any piece of content that you are offering to your clientele should be translated correctly, so everyone hears the message loud and clear.

If the incorrect translation is present, it is observed that customers are often quick to turn away from the product or service – regardless of where the company originates. Not to mention, word of mouth travels fast, and it will not be long before your company is reflected in a bad light.

Moreover, these are just a few challenges that companies face, but the key to it all is to ensure that localization is focused on thoroughly. Not only will that ensure that the transition from one area, region, or even city is smooth, but it will also ensure success in the end. It is always advisable to revise the marketing strategy as best as possible, especially if there is still much room for improvement.

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From an operations standpoint, standardization can seem like the easiest method for expansion, using the same marketing and customer experience style and theme for all your products and services, regardless of the region in which you’re trying to sell them.

However, localization is often more effective, as it allows you to fit in with your new target market’s specific needs by making linguistic and physical adjustments to your existing products and services.

Done correctly, it should decrease barriers to entry, provide customized local customer experience, breed cultural respect and lead to better brand identification, and hasten local business development.

Where should you start when planning your localization strategy?

As with everything in life and business, the key to getting your localization strategy off the ground in the most efficient way possible is research, research, and more research.

Start with a deep dive into your analytics to gain a better understanding of your customers’ preferences and how they might vary from place to place.

Accurate localization means much more than just translating product descriptions into local languages, and there are many essential elements to consider before embarking on such a venture.

Your localization strategy is what will set you apart from companies that merely operate in multiple countries, establishing your business as one that has thriving and long-lasting growth in all your target markets.

Step One: Find out who your customers are.

If you’re looking at a map for directions, it’s essential to find yourself before looking for your final destination and start to plan how you’re going to reach it.

Use a combination of customer surveys and web analytics to find out who your existing customers are, what languages they speak, and other factors that might influence their buying choices, from culture to purchasing power and average income.

Begin by studying potential international customers and try to put yourself in their mindset. What do you have to offer that fits their specific needs, and what are the best ways to reach them? How will that differ from the way you serve your current customers?

Step Two: Decide which market(s) you want to target next.

Based on the information you’ve collated about your current and potential customers; you now have to decide which markets make the most sense for your business to expand into next.

You may want to carry out some trial runs in a few different markets to test the waters, move into one new market at a time, or go worldwide all at once. This medium will be very much dependent on your product, your ultimate goals, and your specific customer base.

Step Three: Research your new market(s).

Start by looking at how your new market differs from the one(s) you already serve. Look at market conditions, in-demand products, typical purchasing power and income, barriers to entry, and local laws and regulations.

Remember that while local markets may be saturated with competitors, that shouldn’t necessarily be a barrier to entry for your business. A large amount of competition can signal that a market is very healthy and ripe for new entrants.

Do some market research ahead of time to help identify culturally relevant features that your competitors might have that will resonate with your target market, set your product apart, and ensure that your future customers gravitate towards your business, not the competition.

Step Four: Define your project goals.

Now that you have a clearer picture of who your new customers might be, where you’re going to find them, and what the local market looks like, it’s time to figure out precisely what it is that you want to get out of this expansion.

What are your specific goals for this project? What are you hoping to achieve? It’s not just about determining how many new markets you want to enter and how many new customers you want to add through that expansion.

Consider the little things as well: how many languages do you want to work with? What marketing strategies do you want to use? How long should the whole process take? How many new employees or freelancers will you need? What is your total budget for the project?

Step Five: Sweat the details.

The main goal of localization is to adapt your business to a new market, so there are a ton of factors that you’ll need to take into consideration. Start with the culture and language of the customers you’re trying to reach.

Local expertise

Consider employing local experts. Who knows a culture better than the local people? Look into and talk to local staff, agencies, and even in-country partners in your target market(s). Don’t forget about the “local” in localized.

Make sure that you have a presence in your new market – in a sense; you want to become “part of the community.” Stay aware of local events and issues, and seek out moments of importance where you can connect with and give back to the local people.

Run market research studies and focus groups – and take the feedback that you receive very seriously. This process will ensure that you understand the dynamics of the local market, customers’ preferences, your competitors, and available marketing channels, before anything else.

Translation

While languages aren’t the be-all and end-all of the localization, being understood in your target market is essential to accessibility, conversion, and – ultimately –sales.

Reportedly, 42 percent of Europeans only shop online in their native language, and 72 percent of online shoppers who aren’t native English speakers would like product reviews to be available in their native language.

You’re going to want to translate your marketing materials and product descriptions into the local language(s), as well as making them culturally relevant. Advertisements that work well in London may not be so easily understood in Tokyo or Paris, even when translated into Japanese and French.

Again, you’re going to want to think about going to the locals for this one, since unintended nuance and cultural differences can lead to misunderstandings and confusion that you never even saw coming, but that a native speaker would spot from miles away.

Money issues

Find out what currency your new market trades in and which payment methods are most popular, and consider pricing strategies. What do similar products cost in your new market, and how much can your new customers afford to pay? How much are they willing to pay?

Learn about relevant local laws and tax regulations. One of the main steps in localization is making sure that you can make your product physically available in a new market. Are their inhibitive customs or tax laws in play, either on your end or theirs?

You might want to consider setting up in-country distribution centers or even employing local manufacturers so that you don’t have to ship anything in and out of the country. In some areas of the world, there may also be marketing restrictions that you’ll need to consider – for instance, Facebook marketing is useless in China.

Design

Choose culturally relevant images, and consider how customers in each market will experience your brand. Find out what customers in each market already think of when they see your company name or logo.

You may very well find that your brand is received differently in different markets and that you will have to work harder to create a new, memorable story for your business in some countries than others. Specific colors may be received differently, and even the name of your brand might have an unintended meaning in some languages.

Digital presence

Finally, think about website design, mobile optimization, and your social media presence. The way that a business presents itself online can be a crucial element in how it is perceived by customers today.

Creating localized content for apps can be of particular importance as it presents a unique opportunity for growth. Having individual apps for each location can vastly increase conversions in those markets.

Look into social networking trends in your target markets, as platforms such as Twitter and Instagram are accessible on a global scale, and are often the primary location in which you will directly interact with your customers.

Different regions and demographics will prioritize different platforms, and localized content in multiple languages is an essential part of any new marketing strategy.

Localization 101 – Do your research and plan ahead

If you’re thinking of taking your business global, localization is the key to a successful launch. And the key to efficient localization is to do your research and make sure that you have a plan.

Before you decide where you want to go, make sure that you know where you are already. Then, look into new customers and new markets through studies and intensive, focused research. Choose the most appropriate markets for your business to target and get planning.

Find out about local cultural norms and differences, and learn how your business might successfully assimilate. Define your goals for the expansion, focusing first on the big picture, and then on individual, measurable milestones that will get you where you want to go.

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Sometimes it seems like the internet is virtually awash in discussions about artificial intelligence, machine learning, and other technologies potentially replacing humans in the workplace.

A robot reporter used by The Washington Post published 850 articles in its first year in action, and Associated Press, a non-profit newswire based in New York City, has started automating some men basketball coverage and quarterly earnings reports using artificial intelligence.

Some journalists have even started to worry that their jobs could be replaced by robot reporters, a feat that would have seemed unthinkable – if not impossible –just a few years ago. The language or translation industries are no different in this regard.

However, it’s unlikely that artificial intelligence-powered translators will be replacing human translators any time soon. Some experts even think that the technology itself is already being over-hyped.

What does it mean?

Simply put, artificial intelligence-aided or machine translation automatically converts text from the source language to text in the output language without the need for a human translator. Theoretically, artificial intelligence can learn more languages than a human, and translate between them at a much faster speed.

There are several different types of machine translation programs that a business can use. The most common are statistical machine translation (SMT-based) – such as Google Translate – and rule-based machine translation (RMBT-based).

The SMT-based translation uses the concept of probabilities to refer to a set of target segments, then chooses the most likely words or phrases as a match to the source segment. I.E., it translates by finding the highest statistical probabilities that a translation is correct.

Conversely, RMBT-based translation operates on the basis that language is all about syntax and grammar rules. Such programs refer to bilingual dictionaries for the specified languages that they are required to translate between.

These dictionaries are made up of linguistic rules that apply to the structure of sentences in each language, and rules to help the program link the sentence structure of each language to each other.

However, the process is time-consuming as new requirements have to be met each time a new language is paired with the source language.

It’s very effective when translating between languages with very different word orders, such as English and Chinese. But, it depends on a much more extensive list of sources that SMT-based programs, which can accommodate a much wider array of languages.

Google’s artificial intelligence-translator can now translate your speech while keeping your voice, according to the MIT Technology Review, after researchers trained a neural network to map audio “voice-prints” from one language to another.

The results aren’t perfect, but the translator was able to keep the voice and tone of the original speaker by converting audio input directly to the audio output without intermediary steps. More traditional systems would typically convert audio into text, translate the text, and then re-synthesize the audio, losing the characteristics of the original voice and tone in the process.

So is it possible that machine translators could replace humans in the workplace?

Artificial intelligence is ubiquitous today. Among other uses, it powers virtual assistants, such as Amazon’s Alexa and Apple’s Siri. It can recognize who and what is in a photo. It has ideas about what you should buy next when you’re shopping online, based on preferences and previous purchases.

There are two broad types of artificial intelligence: narrow and general. The former are intelligent systems that have been taught or have learned how to carry out tasks without specific programming. It has many emerging applications, such as interpreting video feeds from drones, organizing calendars, and helping radiologists spot tumors on x-rays.

General artificial intelligence is more theoretical. It doesn’t exist today but can be found in science-fiction, represented by the likes of Data in Star Trek, HAL in 2001: a Space Odyssey, and Skynet in the Terminator franchise.

It is the kind of intellect possessed by humans – a truly artificial consciousness – and would be capable of learning how to carry out a wide variety of tasks or reason about a wide range of topics based on accumulated experience.

This would be the kind of artificial intelligence translator that would have the ability to replace human translators in the workplace. Current machine translators are narrow artificial intelligence. They are capable of “learning” languages, but they cannot fully appreciate the nuance inherent in human communications.

Machines can’t understand the culture (yet)

Different cultures have different lexical items – like slang, idioms, and proper nouns – that are unique to that specific culture. Machines don’t yet have the complexity to understand or recognize them.

Meanwhile, native speakers who are well versed in the languages into which they are translating, and understand all the slang and idioms of the respective countries, are usually skilled enough to find appropriate equivalents.

Sometimes, a word can mean one thing in one culture, and something totally different in another. Context is important in translation and often completely dependent on human involvement as machine translators can only perform direct word to word translation.

In other words, just because they can translate a word directly, that doesn’t mean that they understand it’s meaning, which can completely alter the translation itself. Similarly, many languages have words with dual meanings, which can be a problem for machine translators.

Languages are, almost by definition, subjective. Artificial intelligence, on the other hand, typically excels at tasks that are rooted in objective reality, functioning best when confronted with clear mathematical or physical rules that govern their decision-making.

However, languages are subjective constructs invented by groups of humans to communicate with each other. While they often exhibit rule-like behavior, this is grounded in the convention, not objective reality, and continuously evolving.

Robots don’t do humor.

Even human translators find jokes, puns, nuanced cultural references, and the occasional bit of sly innuendo hard to get right. Different cultures find different things funny, and differences in spelling and vocabulary can render clever wordplay meaningless.

From an interpreter’s standpoint, tone of voice and body language also directly inform a speaker’s intent and have to be accurately analyzed and conveyed in the target language too. This is challenging for humans and – so far – impossible for a machine.

A move from statistical, phrase-based machine translation to neural networks has resulted in significant improvements in overall quality. But, neural machine translation is now even more dependent on huge sets of training data than previous models.

And since the most significant bilingual data sets available often come from official translations of government documents and religious texts, these algorithms have pitifully low exposure to humor, wordplay, and non-verbal expression.

Furthermore, machine translators can’t admit to – or correct – their mistakes. When Google Translate started offering biblical prophesies in exchange for junk input, for example, experts attributed the errors to neural networks’ preference for fluency over accuracy.

These “false positives” are far more problematic than more obvious mistakes, as audiences in the target language might not realize that a glitch has occurred and could attribute the outlandishness of the translation to the original text itself.

Machines can’t translate on the fly

The above challenges make it difficult enough to perform machine translation on the static text. Asking a computer to translate live speech simultaneously adds several extra layers of complexity, including automatic speech recognition (ASR).

When you have a conversation with Siri or Alexa, they seem to be pretty competent conversationalists, but that exchange is still constrained within a narrow set of context and conditions; short, command-based interactions with a finite vocabulary in a controlled environment.

Most situations in which real-time translation of live speech would be required – such as conferences and business discussions – however, feature speech that is spontaneous, continuous, and often highly dependent on context and specialist knowledge.

Perhaps unsurprisingly, these are traits that send the error rate of most ASR programs soaring. For example, during a speech given by hedge fund guru Ray Dalio, in which he reflected on his mis-forecasts as a young trader, a real-time subtitling machine translator rendered “How could I be so arrogant?” as “Aragon, I looked at myself and I.”

Recent advances in the field have shown some promise. Many experts predict that the word error rate of ASR software will reach parity with human transcribers soon. However, not all word choice errors are equal and won’t always be relatively inconsequential.

Machines aren’t coming for your job (yet)

All of this is to say that it seems unlikely that machine translators will be replacing their human counterparts tomorrow.

While translators and interpreters – alongside copywriters, journalists, and other language economy professionals – may very well lose their jobs to robots at some point in the future, this isn’t going to happen tomorrow.

However, some experts estimate that within the next one to three years, and machine translators will be taking on around 80 percent of corporate translation work.

This doesn’t mean that organizations like the United Nations and police forces won’t need translators, or that we won’t still need sign language interpreters. It is more likely that machine translators will increasingly become just another tool in the arsenal of a good translator.

Computer-assisted translation tools are already widely used among text translators, and while some may object to the idea, simultaneous interpreters could almost certainly benefit from a combination of speech recognition and translation memory technology.

For translators looking to machine-proof their careers – as best as possible – it might be wise to specialize in those languages that are less widely spoken, and encourage the industry to see artificial intelligence as a complement to human output, not a replacement.

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