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Comparing MT Based Translation Errors with Human Translation Errors

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This is an important subject and one that needs ongoing examination and continuing study to get to real insight and find greater process efficiency. I hope that this post by Silvio Picinini will trigger further discussion and I also invite others who have information, and opinions to share on this subject, to step forward. In my observation of MT output over time, I have seen that MT produces a greater number of actual errors, but the types of errors most often generated by MT are easy to spot and clean up. Unlike the incorrect or inconsistent terminology and sometimes misunderstood source errors, that may often be hidden in the clear grammar and flow of a human translation. These human errors are much harder to find, and not as easy to correct without multiple stages of review by independent reviewers.

It is common to see a focus on the errors that machine translation makes. But maybe there are also strengths in MT that can benefit translators and customers. So I wrote an article in Multilingual magazine comparing errors made by MT and human translators. The article is below, reproduced here with permission.

Please find the original article published on Multilingual magazine, July-August 2016, “Errors in MT and human translation”, pg. 46-50.
Post-editing could be defined as the correction of a translation suggested by a machine. When a translator receives a suggested translation from Google, Bing or another machine translation (MT) engine, the translator is working as a post-editor of that suggestion.

If instead the translator receives a suggestion from a translation memory (TM), the suggestion for that segment was created by a human translator in the past. If there is no suggestion from a TM, human translation is the creation of a translation by a human. If we now assume that a human translator and an MT engine “think” differently about the translation of a sentence, we can explore how a human translator makes different errors compared to a statistical machine translation process. If we make translators and post-editors more aware of the types of errors that they will find, they will be able to improve their translation quality.


A human translator will likely go through a text and consistently translate the word “employee” with the same words in the target language, keeping his or her favorite word in mind and using it all the time in the translation. The MT engine, on the other hand, has no commitment to consistency. Statistical machine translation (SMT) is a popularity contest. So, in one sentence, the translation for “employee” may be the usual one. But the corpus used to train the engine may have come from governments, or from the European Parliament, for example. And governments may like to call employees “public servants.” If that is popular enough, the MT may choose this translation for some sentences. The translation will be inconsistent and could look something like “Company employees may only park on the east parking lot. If that is full, the public servant may park on the west parking lot.”

Thus, MT is more likely than humans to translate inconsistently.

However, here are two more thoughts on this. First, humans create inconsistencies also. If the TM contains translations made by several translators with different preferences for words, or contains translations created over a long period of time, chances are that there are inconsistencies in the TMs. Those inconsistencies will be accepted by the human translator.

Second, we are not weighing glossaries to one side or the other. The human translators could follow the glossary, or run glossary consistency checks and their translations would be consistent. But the same applies to post-editing.

Many words have more than one meaning, and are defined as polysemous. The word “holder,” for example, may mean a person who owns a credit card (as in credit card holder), but it may also be a stand designed to hold an object, such as a plate holder. If you are thinking of items on eBay, you are most likely expecting the translation that means plate holder. A human translator will easily know the correct translation. The machine, however, may have a lot of training data related to finance or laws, and the most popular translation there could be the person. The MT could choose this most popular meaning and the result could be “fashion sponge holder with suction cup” translated as “fashion sponge card holder with suction cup.”

Thus, MT is more likely than humans to make mistranslation substitutions.

Words that are not to be translated

For a human translator, it is easy to know that Guess, Coach and Old Navy are brands, and therefore should not be translated into most target languages. As you know, SMT is a popularity contest, and the very common word “guess” likely appears quite frequently in the corpus that trained the SMT engine. The consequence — you guessed it — is that the MT is likely to translate the word instead of leaving it untouched because it is a brand.
This may happen with product names as well. Air Jordan sneakers could have the word Air translated. It could happen with the brand Apple versus the fruit apple, but since it is a popularity contest, the fruit may now be left untranslated instead of the iPhone having a fruit to go with it.

Thus, MT is more likely than humans to translate words that are not supposed to be translated.

Untranslated Words

The MT leaves out of vocabulary (oov) words untranslated, and humans will translate them. This will favor humans depending on how many oov words are present in the content to be translated. If the content is specific and different from the corpus used to train the engine, it is more likely that some words will not be known by the MT engine. But if the MT engine is well trained with the same kind of subject that is being translated, then the MT engine will minimize the number of untranslated words. On the other hand, MT takes the collective opinion into account. It may not translate words that are now commonly used untranslated, while a translator could be more traditional or old-fashioned and would translate the word. Would you translate “player” in “CD player” in your language? The word “player” used to be translated a few decades ago, but the usage changed and the English “CD player” is common now in many languages. The MT will learn from the corpus the most current and frequent usage, and may do better than a human translator. Overall, this issue still slightly favors the human side.

Thus, MT will leave more wrongly untranslated words than humans.

Gender and Number Agreement

The MT engine may learn from the corpus the correct translation for “beautiful skirt,” and skirt is a feminine word in many languages. However, the first time the source contains the combination “beautiful barometer,” it will pick from what it knows and it may translate beautiful as a feminine word. If barometer is masculine in the target language, this creates an error of gender agreement. The MT is more likely to make this error than a human translator, who intuitively knows the gender of objects. The same applies to singular and plural. English uses invariant adjectives for both, as in “beautiful skirt” and “beautiful skirts.” Thus, the MT engine may pick the singular translation for the adjective next to a plural noun. The MT is more likely to make a number agreement error than a human translator, who knows when singular or plural is needed.

Thus, MT will make more grammar errors than humans.

So far we have seen several examples of situations where humans translate better than MT engines. Now we will look at how a “self-correcting” property of MT, created from the popularity of a translation, can often do a better job than humans. A statistical MT engine can be seen as a “popularity contest” where the translation that is suggested is the most popular translation for a word or group of words present in the “knowledge” (corpus) that trained the MT engine.

There are two types of spelling errors: the ones that create words that don’t exist (and can be caught by a spellchecker) and the errors that turn a word into another existing word. You may have turned “from” into “form” and “quite” into “quiet.” The first type, a pure spelling error made by MT would require that you have in the corpus more instances of the error than of the correction. Can you imagine a corpus that contains “porduct” 33 times and “product” only 32 times? So MT almost never makes a spelling error of this kind.

For the second type, humans turn words into other words, and the spellchecker will miss it. The MT engine will not make this error because it is not likely that the corpus will contain the misspelled word more frequently than the correct word for that context. This would require having “I am traveling form San Francisco to Los Angeles” more frequently in your corpus than you would have “I am traveling from San Francisco to Los Angeles” and which one is more likely to be popular in a corpus? The correct one. This is why MT will almost never make this kind of spelling error, while it is easy for a human translator to do so.

Thus, humans are more likely than MT to make spelling errors of any kind.

False Friends

False friends are words that look similar to a word in a different language, but mean something different. One example is the word “actual,” which means “real” in English. In Portuguese, the word atual means current or as of this moment. So a presentation mentioning “actual numbers” could be translated as “current numbers,” seriously changing the meaning and causing confusion. A human translator may make this error, but the MT would require the wrong translation for “actual” to be more popular in the corpus than the correct translation. You would need “actual numbers” to be translated more frequently as “current numbers” than as “real numbers.” Do you think this would happen? No, and that is why MT almost never falls for a false friend, while a human translator falls for it occasionally.

Thus, humans are more likely to make false friend errors than MT.

Fuzzy Match

There are several errors that result from the use of TM. These memories offer the human translator suggestions of translation that are similar to the segment they are translating. Similar does not mean equal, so if the suggested translation is a fuzzy match, the human translator must make changes. If they don’t make any change and accept the fuzzy match as it is, they risk making errors. There are three sub-types of errors to mention here:

Different terms. Think of a medical procedure where the next step is “Administer the saline solution to the patient.” If a fuzzy
match shows “Administer the plasma to the patient,” this might risk a person’s life.

Opposite meaning. Think of “The temperature of the solution administered to the patient must stay below XX degrees.” If a fuzzy match shows “must stay above XX degrees,” this might risk a person’s life. For an eCommerce environment, this type of error could be a major issue: “This item will not be shipped to Brazil” versus “This item will be shipped to Brazil.”

Numbers that don’t match. Fuzzy matches from a year before may offer the translator a suggested translation of “iPhone 5” because that was the model from a year ago. The new model is the iPhone 6. If a fuzzy match is accepted with the wrong number, the translator is introducing an old model.

Thus, humans are much more likely to make errors for accepting fuzzy matches than MT.


MT may leave acronyms as they are, because they may not be present in the corpus. The MT engine has the advantage of having the corpus to clarify if an acronym should be translated as the same acronym as in the original, if it should be translated as a translated acronym or if it should be translated using the expanded words from the meaning of the acronym. Human translators may make errors here. If they do research, and the research does not clarify the meaning, the original acronym may be left in the translation. So this is an issue that favors the MT over humans, although not heavily.
The best solution for both humans and MT is to try to find the expanded form of the acronyms. This will help MT and humans produce a great and clear translation.

Thus, humans are slightly more likely to make errors translating acronyms than MT.


MT may handle terminology remarkably better than a human translator. If an engine is trained with content that is specific to the subject being translated, and that has been validated by subject matter experts and by feedback from the target audience that reads that content, the specific terminology for that subject will be very accurate and in line with the usage. Add to this the fact that multiple translators may have created those translations that are in the corpus, and it becomes easy to see how an MT engine can do a better job with terminology than a single human translator, who often translates different subjects all the time and cannot be a subject matter expert on every subject.

Consider the following example:
English: “In photography and cinematography, a wide-angle lens refers to a lens whose focal length is substantially smaller than the focal length of a normal lens for a given film plane. This type of lens allows more of the scene to be included in the photograph.”

Portuguese machine translation: “Em fotografia e cinematografia, uma lente grande angular refere-se a uma lente cuja distância focal é substancialmente menor do que a distância focal de uma lente normal para um determinado plano do filme. Este tipo de lente permite que mais da cena a ser incluída na fotografia.”

In this example about photography, the MT already proposed the translation of wide-angle as grande angular, which is the term commonly used to refer to this type of lens. This translation means approximately “large angular.” A human translator knows the translations for the words wide and angle. The translator could then be tempted to translate the expression wide-angle literally as “wide angle” lenses (lente de ângulo amplo), missing the specific terminology used for the term. The same could happen for focal length. Portuguese usually uses distância focal, which means “focal distance.” A human translator, knowing the translation for focal and length, would be tempted to translate this as comprimento focal and would potentially miss the specific terminology distância focal.

The quality of the terminology is, of course, based on the breadth and depth of the corpus for the specific subject. A generic engine such as Google or Bing may not do as well as an MT engine custom-trained for a subject. But overall, this is an issue that could favor the MT over humans.

Thus, humans are more likely to make errors for inappropriate terminology than MT.

2016-09-02_1016 Figure 1: MT and human translation errors

Emerging technologies for post-editing

Now that we are aware of the issues, which are summarized in Figure 1, we are in a better position to look at emerging technologies related to post-editing. Post-editing work has one basic requirement: that the translator is able to receive MT suggestions to correct, if need be. This technology is now available integrated on several computer-aided translation (CAT) tools.

Considering the above, the next application of technology is the use of quality assurance (QA) tools to find MT errors. The technology itself is not new and has been available in CAT tools and in QA tools such as Xbench or Okapi CheckMate. What is new is the nature of checks that must be done with these tools. One example: in human translation you use a glossary to ensure the consistent translation of a term. In MT, you could create a check to find a certain polysemous word and the most likely wrong translation for it. Case frequently has the meaning of an iPhone case, but it is often wrongly translated with the meaning of a legal case. Your glossary entry for MT may say something like “find case in the source and legal case in the target.” This check is very different from a traditional check for human translation that looks for the use of the correct translation instead of the use of the “probably wrong” one.

After doing post-editing and finding errors, the last area of application of this technology is in the measurement of the post-editing, since what makes post-editing most attractive is the promise of increasing the efficiency of the translation process. We will briefly mention some of the main technologies being used or researched:

Post-editing speed tracking. The time spent post-editing can be tracked at a segment level. These numbers can then be compiled for a file. Some examples of use of this technology include the MateCat tool, the iOmegaT tool and the TAUS DQF platform.

MT confidence. Another technology worth mentioning is MT confidence scores. Based on several factors, an MT engine can express how confident it is on the translation of a certain word. If this confidence can be expressed in terms of coloring the words in a segment, this feature will help the post-editor focus on words with less confidence that are therefore more likely to require a change. This feature appeared in the CASMACAT project, as illustrated in Figure 2.

2016-09-02_1019 Figure 2: The CASMACAT project shows MT color-coded according to what is most likely to need post-editing

Edit distance. A concept that is not new but could be more used more often is the concept of edit distance. It is defined as the number of changes — additions, deletions or substitutions — made to a segment of text to create its final translated form. Comparing the final form of a post-edited segment to the original segment that came out of the MT engine provides a significant indication of the amount of effort that went into the post-editing task. The TAUS DQF platform uses edit distance scores.

We use the concept of edit distance in a broader sense here, indicating the amount of changes. This includes the “raw” number of changes made, but also includes normalized scores that divide the number of changes by the length of the text being changed, either in words or characters. The TER (Translation Edit Rate) score is used to measure the quality of MT output, and is an example of a normalized score.

The final quality that needs to be achieved through post-editing defines levels of “light post-editing” and “full post-editing.” There are discussions to define and measure these levels. The scores based on edit distance may provide a metric that helps in this definition. It is expected that the light post-editing should require fewer changes than a full post-editing, therefore the scores based on edit distance for a light post-editing should always be lower than the score for a full post-editing. Figure 3 below shows a hypothetical example with numbers.

2016-09-02_1020 Figure 3: Edit distance shows how much editing is required for a given machine-translated file.

Scores based on edit distance can be an important number in the overall scenario of measuring post-editing, combined with the measurements of speed. The TAUS DQF efficiency score proposed a combination of these measurements.

Four SEO factors to consider before translating your website

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Online business and e-commerce is being increasingly driven by local search. This has seen companies providing their target audiences with an ever more detailed and localised web experience.
The most successful international retailers sell in the customer’s native tongue and cater to their expectations and tastes. They provide the goods and services that the market demands and cater to the local currency.
Few brands succeed in the competitive retail space with a web presence that appears distinctly foreign and shows little knowledge of the local market or cultural nuances. Ensuring that your website is accurately and astutely translated and localised is therefore one of the most important steps towards establishing an international client base.

But while your website may be the shop window for your business, you need to ensure that the shopper can, first, find your shop and, second, be sure that the products are indeed readily available to them.
International SEO goes beyond translating your website copy, adapting culturally suited images and re-wording your calls-to-action (CTAs). In fact, the process begins before the first word on your website has even been translated.
We’ve rounded up the most important factors to consider on your international SEO journey, from gathering and harnessing an understanding of your target market, to deciding on how you want to structure your website domain.

1. Justify your international presence

Ask yourself: is there sufficient consumer demand in your selected target markets to justify a localised presence?
How you choose to quantify this justification is up to you. You may wish to rely on data and market forecasts. Or you may want to base it on online user behaviour. Google’s Keyword Tool in Adwords will give you an idea of the local search volume for your product or service. Search engines popular in other countries, such as Yandex in Russia and Baidu in China, offer similar keyword research services.
Also look closer to home and analyse how overseas audiences are engaging with your domestic website. For example, is it attracting traffic – and potentially conversions – from aboard? Google Analytics gives you a comprehensive breakdown of user behaviour and page visits based on location. High international visitor numbers suggest that an international audience is already interested in your brand and what you offer. Now it’s time to begin planning your website. Read More

Some Exciting Tools for Translators and Interpreters

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There are some great programmes, apps and tools out there to help with various aspects of the workflow of Translators and Interpreters. From e-mail clients that stop email trackers or turn your e-mail into a virtual CRM. Others improve writing and blogging, self-development and productivity.

Here I have chosen 3-4 programmes / apps to help with each of these aspects. If you are interested in any of these, I highly recommend spend a little time on the Net researching them, there others out there that did not make it onto this list.

Document Signing Apps

Have you ever arrived at an assignment and realised you’ve left the printed job sheet that your client needs to sign on the kitchen table? That is no longer a problem, in this increasingly digital world, that document can now be signed digitally! Many of these send the document to all signatories separately in sequence, with it being returned back to the sender once finished, providing tracking information along the way so you know how far along the process is. Some have a charge on download with no additional cost, while others are free but charge a usage fee. As with the translation industry ‘you get what you pay for’, there is always a catch – if it is completely free it is probably not worth it. It is worth investigating the pros and cons of each and being aware of what the charges are for and find the one that suits your uses the best. Read More

Language & style – translation

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If you are a journalist working in a multilingual society, you may have to work in more than one language. Whether you gather the information in one language and write the story in another, or whether you write a story first in one language and then rewrite in another language, you face the task of translation. However, if you have a good command of both languages and follow a few simple rules, translation should not be difficult.

The previous three chapters on language and style have looked at structure, words and grammar. In this, the final chapter in this section, we provide some general guidance when working in more than one language. This is written only in English, but the processes we describe always involves two or more languages. It is possible that English will not be one of the languages you work in when translating. To avoid confusion, we will call the language which you are translating from (or conducting interviews in) the source language; and we will call the language you are translating into (or writing the final story in) the target language.

The principles of translation

The first thing to remember is that translation is the transfer of meaning from one language to another. It is not the transfer of words from language to language. You must translate the meaning of what is being said, rather than do it word-for-word. This is because languages are not just different words. Different languages also have different grammar, different word orders, sometimes even words for which other languages do not have any equivalents. The English spoken by a scientist may have words which a simple farmer cannot even start to imagine. And the farmer is likely to have words for things the technologist never dreamed of.

Simple steps in translation

We will start by talking about the simplest form of translation – the one where you already have a story written down in one language (the source) and you want to translate it into another language (the target). The steps to follow are:

Read the whole of the original source story through from beginning to end, to make sure that you can understand it. If you cannot understand everything that is said, you cannot translate it. If there are any words or phrases that you do not understand, you must clarify these first. You may decide that the ideas they express are too difficult to translate or not worth translating, but you need to know what they are before you can judge.
Do a first draft translation, trying to translate all the source material. But do not translate word-for-word. Remember that you are translating the meaning. When you have finished the first translation, you will now have a draft story in the target language.
Go back over the whole of your draft translation and polish it without looking at the source original. (You might even like to turn the source story face down on your desk so you cannot cheat.) Make sure that your translation reads well in the target language.
Compare the final version of your translation with the source original to make sure that you have translated it accurately. This is when you can make any detailed adjustments in individual words or phrases.

False friends

Beware of words or phrases we call “false friends”. These are words in the original source language which you retain in your translation, often because you cannot think of the correct translation. If you cannot think of the right word, how can you expect your reader or listener to? Of course, languages borrow from each other all the time. If a society comes across a new idea, it may simply use the foreign word without inventing a word of its own. Remember, however, that you are translating meaning, not words. If you come across a word in your original language which has no equivalent in the target language, perhaps you can use a phrase (i.e. several words) instead. For example, many languages do not have a word for “computer”. Instead of retaining the English word “computer”, can you translate it as “a machine which does brain work” or something similar? Be careful, though, that you do not try to re-invent the community’s language to suit your own way of thinking. If you have problems with translating words, consult experts or ask your colleagues to see if you can reach agreement on the correct translation. If you are a journalist working in a small language community, the words you decide upon could become the standard usage.

Writing style

You do not have to be an expert in linguistics to make good translations. If you know your target language well, you can usually hear in your head whether the sentence sounds correct in your translation.

Your translation should not try to duplicate the word order or grammatical construction used in the source language unless it is also correct in your target language. For example, some languages put the verb (the “doing word”) at the beginning of a sentence, some in the middle and some at the very end.

You do not have to use all the words from your source material for translation if your target language can cope without them. For example, we may say in English “The ship sank lower in the water”, whereas in another language the words “in the water” may be unnecessary because the words for “sink” in relation to “ship” already includes the idea of “water”.

Also, do not be afraid of using more words in your translation than in the original. Although in journalism you should aim to keep your sentences short and crisp, this must not be allowed to interfere with the clarity of the ideas you are trying to communicate.

Some other problem areas

Translation is a very big and complicated field which we cannot discuss in great detail here. However, the following are some other problem areas you might want to keep in mind:

Understatements and euphemisms

Be aware of the cultural differences in languages. Some languages like to hide unpleasant facts beneath understatements or euphemism. Euphemisms are mild or inoffensive words which are used in the place of harsh or hurtful words.

Some speakers might use humour in one situation which another language would not permit. Again, you must understand the meaning in context.

Linking words

Words such as “although”, “but”, “from”, “even” and a host of others are usually very important in English, as they are used to show the relationships between the words in your sentences. Getting these small words wrong can alter entirely the sense of the sentence.


These can sometimes cause problems in their different forms. There are, for example, quite distinct meanings for the words “can”, “may”, “must” and “should”. If you are not sure, it is best to avoid the construction altogether and say it a different way.


Some languages are more accurate than others in certain areas. For example, many language groups in Papua New Guinea have more than 10 different words for varieties of sweet potato. The Inuit Indians of Canada have different words for 20 separate things which in English we just call “snow”.

English is not a precise language in many areas. Be aware that a vagueness in English may not be acceptable in another language. For example, we can say “Doctor Smith” in English, whereas in Chinese we have to know the gender of the doctor to translate the word “doctor”.


Sometimes the exact meaning in the source language is left unclear (ambiguous) on purpose, in which case you should try to keep it that way. This is especially so when reporting claims, accusations and hearsay evidence in such things as police stories. For example, a person might be charged in English with “unlawful carnal knowledge”, which usually means a sexual offence against a person under the age of consent. You should not translate that as “rape of a child” or “sodomy of a little boy” or any other specific sexual act unless that is part of the charge. It is better in this case to use a phrase similar to “a sexual offence against a young person”.

The Essentials of PPC Keyword Research for multilingual web

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Identifying keywords for Pay Per Click (PPC) ad campaigns is a dynamic and never-ending process that demands continuous improvement. The key to conversion is within your keywords and how effectively you use them. Data-driven keyword research is a must to remove any guesswork from the process. The idea is that outcomes of a PPC campaign should not be a surprise once you have agreed to invest in it.
But how do you start and from where? Do you have the right approach when searching for the best business keywords to suit your campaign? Here are a few ideas to start with before you jump into your PPC campaign.
The manual exercise of generating keyword ideas can limit your options and opportunities. Keywords, if not evaluated against monthly search volume, location popularity, CPC rate, competitive pricing and ad position, may ruin the entire marketing effort. It is important that you make informed decisions while choosing and applying keywords for your business campaign.
Know Your Keyword Search Tools and Their Usage First
Using keyword research tools like Google AdWords Keyword Planner, SEMrush Keyword Analytics, WordStream Keyword Tool, Moz, Wordtracker, or SEO Book can be of great help.
Another interesting bit of news for the PPC marketer is that SEMrush has come up with its very own PPC Keyword tool which is in beta stage. With this tool, not only can you create new PPC keywords in various combinations but also collect competitors’ keyword ideas for implementation. SEMrush has developed its own database for searching new keywords. It also allows you to discover intersecting keywords in different groups and add them as negatives in order to avoid ad groups’ competing.
However, which tool to go for and what to try out for occasional use depend on individual choice. There is no guarantee that using these tools will bring you higher conversions, but they can definitely help you in picking the right keywords that influence your PPC campaigns.
Using Keyword research tools can automate data capturing, data storing and sorting processes, and provide important statistics that marketers need for quick decision making. The steps to using keyword tools are simple.
Learn the must-have features you should find in a keyword research tool.
Evaluate the keyword tool based on its ease of use and level of automation.
Establish the targeted campaign settings — location, device, placements, schedule, targeted ad message and landing pages — before running the keywords.
Keep on evaluating Clicks, CTR, Conversions, Cost per Conversion for each keyword and improve upon their number and variety for better outcomes.
In this article, we will focus on the key capabilities of a PPC keyword research tool and how you can use one to your advantage. Following the checklist will insure you choose tools that will have a positive impact on your overall campaign objectives.
Tool Should Provide Well-Tried and Tested Keywords
A good tool can help you perform PPC keyword research for both Google AdWords and Bing Ads campaigns. If you have a list of all relevant keywords that your competitors and industry leaders are already competing and ranking for, it becomes easier to set short-term and long-term PPC targets. The tool must have the historical database to provide you with performing keywords in your targeted domain.
Tool Should Provide All Useful Information About Selected Keywords
Extended information such as average monthly search volume by location, search trend charts, Keyword Difficulty (KD) score, CPC, Competitive Density and number of results in SERPs provide you better insights about a keyword. Evaluating a keyword based on all these essential metrics helps you optimize the list of prospective keywords and build on it. Read More

We Translate, Inc. – Digital Marketing Professional (SEO-SMO-SEM)

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Are you a digital marketing professional with an aptitude for social media, digital advertising, PPC and SEO?

Do you have a flair for creating and curating content as well as writing flawless, engaging copy for social media and web?

Are you experienced and well versed in using LinkedIn, Twitter and Facebook; responding to posts and reporting on activity?

If the answer is yes to all these questions …… you might be perfect for a new and exciting opportunity we have!

The Digital Marketing Officer role is an exciting opportunity to help shape the social media strategy for the company. You will be responsible for providing digital support to the Marketing and People and Performance teams to enhance the presence of and promote We Translate through social media and other channels. You will use social media effectively to increase brand awareness of We Translate recruit new team members and support lead generation.

You will need to:

Be a graduate
Have a thorough understanding of Google analytics, Facebook insight, Twitter analytics and LinkedIn analytics
Have a good understanding of SEO – SMO – SEM
Have proven experience in looking after company social media platforms and writing copy for social media and web.
Have experience delivering marketing campaigns through a range of digital channels including Facebook
Have excellent written communication skills, grammatical accuracy and attention to detail
Have an engaging, proactive and creative approach to content creation
Be self-motivated, proactive and have the ability to multi-task
Be a good team player

Send your CV’s at :