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The purpose of my self-learning task is to investigate whether machine translation used by the CAT tool MateCat is capable of producing an adequate translation of a specialized text. In order to achieve my goal, I will create and import a glossary consisting of the specific terminology needed for this type of text. Then I will compare the version provided by machine translation with my glossary.

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Reasons for choosing MateCat as my field of research

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Reasons for choosing MateCat as my field of research

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Just like OmegaT, MateCat is a free program accesible to anyone. At first glance, it is straightforward and easy to use (in my opinion, easier than OmegaT). Unlike other CAT tools, MateCat is avaiable online and cannot be downloaded. However, most importantly, MateCat offers unlimited machine translation services, for which OmegaT users have to pay.

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Step 1:                                                                

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Step 1:                                                                

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28.03.2025 - First, I had to name my project, select the relevant language pair, choose the subject related to the source text (not required) and upload the ST.

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28.03.2025 - Then, I created my private Translation Memory. I learnt how to do that from this user manual: https://www.amtaweb.org/AMTA2014Proceedings/AMTA2014Proceedings_MateCatManual.pdf.

To begin, I clicked CREATE RESOURCE, and when a new window appeared I selected NEW RESOURCE. After that, it was required to insert the name for the resource and click the Confirm button.

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28.03.2025 - After creating my own private TM, I tried to import a glossary, which I produced in Excel. I learnt how to do that from this website: https://guides.matecat.com/ (in the Language assets tab).

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When I uploaded my file, an error message appeared. Following the link (https://guides.matecat.com/glossary-file-format), I found out that I needed to use a template provided by MateCat.

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I inserted the language pair specific to my project and copied terms from my initial glossary to the proper columns. Afterwards, I uploaded the file again and confirmed it.

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28.03.2025 - The fourth step included learning how to enable the built-in machine translation (Modern MT Lite). In order to achieve that I clicked the Machine Translation tab. I discovered that MT was already on and it could also be disabled in an easy way. It is much simpler to use, free and not limited in comparison to OmegaT.

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Afterwards, I went back to the window from the Step 1 and pressed the button Analyze (containing all the information about the word count and how much time one saved with MateCat) and then Translate.

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28.03.2025 - Thanks to machine translation, the first segment was translated immediately. There were Translation Matches under each segment; in this case there were 3 matches: provided by MT, public Translation Memory and public corpora. Even though MateCat assures that it offers more matches than any other tool, in most segments it failed to show matches from TM and public corpora, providing only suggestions made by machine translation.

The next thing I did was to go through each segment one by one and accept propositions provided by TM with the shortcut visible in the picture.

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28.03.2025 - The sixth step was comparing MT's equivalents of specific terms to my glossary.

MT falsely assumed that sucking loach and spined loach (kózka) are the same fish and translated the term incorrectly. Consequently, it created kóźka ssąca, which does not exist.

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Moreover, it seems MT can not handle idioms.

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Here, MT failed to understand the meaning of the word tankmates in this specific context, creating a ridiculous translation.

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In this instance, MT inserted a wrong equivalent of the source word specimens for this specific context.

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28.03.2025 - Another thing I decided to do was to check how MT tackles whole sentences.

In this case, the target sentence does not carry the same message as the source one. The correct translation should be: W sklepowych akwariach młode glonojady syjamskie żyją w skupiskach, ale w miarę wzrostu stają się coraz bardziej agresywne i zaczynają bronić swojego terytorium. I did not change the second part of the sentence because it sounds right. It indicates that TM's propositions can be a base for one's translation.

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Despite being lengthy, this suggestion is quite good and does not require any adjustments.

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Here is another example of the target sentence not being a proper equivalent of the source sentence. This suggestion is actually not a good base for translation.

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Here, besides inserting a wrong equivalent of the word specimens, MT suggested translating stomache as żołądek, even though in this context the meaning of the word is the exterior of the sucking loach.

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29.03.2025 - Next, I decided to test if MT could handle non-specialized texts that are easy to understand and do not require a glossary.

Out of the 17 segments, MT translated only 1 completely incorrectly and only 2 in an unnatural way.

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These sentences were translated too literally, even though they are not complicated. However, they are still understandable.

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In this case, the sentence is wrong and unintelligble. MT chose an improper equivalent of the word came, even though the sentence is A1-level.

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29.03.2025 - My last step was to add to MateCat the machine translator called Lara which is said to be more advanced than Modern MT Lite. Moreover, I wanted to check if Lara would handle the same texts better than the built-in MT.

In order to achieve that I created a new project and clicked on Add MT Engine in the window from Step 4. Then, I chose Lara from avaiable providers. It turned out that I needed the Acces Key ID and the Access Key Secret in order to enable this MT.

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After pressing the Learn More button, the website https://laratranslate.com/about-lara opened. I needed to create an account and select a plan (there were free and paid ones). Then on this website https://guides.matecat.com/lara I learnt how to get required keys.

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The world's most reliable translator

Lara is unique. It explains its translation choices to ensure clarity and alignment with your intent, values transparency and builds trust by acknowledging its current limits.

Lara is better than machine translation, close to the best professional translators

Lara is the best translation technology ever created

In a double-blind evaluation, professional translators consistently preferred Lara over leading machine translation systems. The test set comprised user-generated content (chats, reviews, product descriptions) in the ten most requested localization languages.

Professional Translators Prefer Lara Quality* (%) 40% 30% 60% 50% 70% 80% Finance Technology Tourism General Lara OpenAI's GPT-4o Google Translate DeepL

* Percentage of time that at least 2 out of 3 professional translators agreed that a translation was accurate in 2,700 translations from English to Italian, French, Spanish, German, Portuguese, Japanese, Chinese, Russian, and Korean.

Lara is approaching the quality of the best professional translators

Lara delivers more accurate, natural, and trustworthy translations than the median professional translator. Early evaluations of the new model, set for 2025, project it to reach and surpass the language singularity - when AI will match the quality of the top 1% of professional translators.

Our Progress Toward Language Singularity January 2024 January 2025 Language Singularity Typical Machine Translation System Lara Grande 0 12 6 10 8 4 2 Lara Median Professional Translator Top 1% Professional Translator Errors per 1,000 Words *

* Percentage of time that at least 2 out of 3 professional translators agreed that a translation was accurate in 2,700 translations from English to Italian, French, Spanish, German, Portuguese, Japanese, Chinese, Russian, and Korean.

How Lara works

Understands context

Lara analyzes the entire text or document to understand the relationships between sentences, ensuring a natural flow and consistency throughout the translation. By maintaining a full contextual understanding, Lara accurately reflects the original intent while enhancing readability for the target audience.

Explains choices

Lara articulates its translation choices using a unique dataset of 25 million real-world translations by top professional translators. It includes machine-generated translations reviewed and refined by two professional translators, capturing errors, corrective feedback, and reasoning during disagreements. This knowledge helps Lara gather insights, weigh options, and refine translations, ensuring reliable results.

Adapts styles

Users can request three different styles of translation from Lara: Faithful (for manuals, legal, and technical content), Fluid (for general content), and Creative (for marketing, literature, and creative writing). If something is unclear, Lara asks questions or requests instructions, such as which gender or what context, to refine translations and meet user needs precisely.

Gives you access to language experts

Lara, at a very reasonable cost, provides access to language experts. These include managing complex requests, reviewing translations by native-speaking language professionals, handling web localization, desktop publishing, and performing cultural checks for appropriateness.

Our quest for trust in translation

Lara is the culmination of more than a decade of research in machine translation with the mission of enabling everyone to understand and be understood in their native language.

First adaptive machine translation

The most-cited MT scientists form a group and are backed by EU to create Matecat, the first translation tool including adaptive machine translation.

A more human machine translation

A new team from the Matecat group unveils ModernMT, the first commercial application of the Transformer, the foundational model of generative AI.

The world's best machine translation

ModernMT, now supporting 200 languages and capable of learning from data based on its trustworthiness, is a leading MT in Gartner, CSA Research and IDC MarketScape evaluations.

The most reliable translation AI

Lara debuts human-like quality in machine translation, enabling a transition to a world where every piece of content is automatically delivered in the user's preferred language.

Reaching language singularity

By 2025, the new Lara model is expected to match the quality of the top 1% of professional translators in the most widely used language pairs.

Start translating with Lara

For individuals and teams

Lara is available as an online translation tool you can trust for your personal and professional life, whether you need to translate a text or a document. For more complex tasks, you can rely on Lara's live multilingual project manager support.

For enterprise applications

Lara seamlessly integrates into your localization workflow and connects with your content source. With Lara, you can dramatically improve localization quality, reduce costs, translate an order of magnitude more content, and deliver an incredible experience to a broader global audience.

Inspired by the story of a little girl

Lara was inspired by a little girl born with the gift of speaking every language in the world

Language has been the most important factor in human evolution. Through language, we can understand each other and work together to build a better future. Complex language has enabled us to advance faster than any other species.

By enabling everyone to understand and be understood in their native language, we are unlocking the next stage of human evolution. We believe in humans.

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Lara is unique. It explains its translation choices to ensure clarity and alignment with your intent, values transparency and builds trust by acknowledging its current limits.

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Lara vs. Modern MT Lite (in a specialized text)

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Lara vs. Modern MT Lite (in a specialized text)

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In this case, Lara gave a different proposition that Modern MT Lite, though still incorrect and ridiculous. Moreover, Lara translated loaches as polany, while the built-in MT suggested kęski for which I cannot find any explanation. (29.03.2025)

Update (13.06.2025):

After 3 months, Lara translated wide bodied tankmates as szerokobrzuszate towarzysze akwarium, which is definitely better than szeroko rozwinięci towarzysze czołgów, but still isn't entirely accurate. Moreover, polany changed to węgorze, which is still incorrect. It also seems that Lara's second attempt is less wordy.

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When it comes to the idiom to be tarred with the same brush, the word specimens and the term the sucking loach, Lara gave the same translation as Modern MT Lite. (29.03.2025)

Update (13.06.2025):

When Lara translated the same text almost 3 months later, the following changes occured:

the idiom wasn't translated literally and it fits in this context (Lara handled it better than the built-in MT but only after some time)

the word specimens was also translated accordingly

when it comes to the term sucking loach, Lara used the word wijęnica, which doesn't exist.

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As for whole sentences, Lara handled the examples from the Step 7 in the same exact way. Except for the sentence above which was translated correctly, in comparison to Modern MT Lite (if we ignore Lara's mistranslation of the term sucking loach). (29.03.2025)

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Lara vs. Modern MT Lite (in a non-specialized text)

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Lara vs. Modern MT Lite (in a non-specialized text)

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In comparison to the built-in MT, Lara translated this sentence correctly.

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Lara inserted phrase blisko gór instead of obok gór, as was the case of Modern MT Lite, creating sentence that sounds naturally.

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Here, Lara chose the male gender form of the verb and adjective, while the built-in MT did the opposite. Nevertheless, both propositions of the sentence above seem strange, too literal.

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My learning goal was to find out whether machine translation in MateCat can handle specialized texts well. In order to do that, I learnt how do add a glossary for comparison. I discovered that the built-in MT has problems with choosing proper equivalents of terms in the TL. Furthermore, there were cases where it translated sentences unnaturally or too literally. However, sometimes MT delivered propositions which were pretty good or could be an acceptable base for one's translation. Another thing with which MT struggled was selecting a correct equivalent of a word from ST that would match the context. Moreover, I decided to check if Lara (reportedly more advanced and context-aware) could tackle it better. The results provided by the two programs were similar. (29.03.2025)

Update (13.06.2025):

The second time working with Lara, I could really tell that it does a better job at translating into Polish than the built-in MT. It seems that Lara is constantly learning and being improved. It didn't have trouble with the idiom and converted the measurements, which I appreciated. However, it still struggled with terms, sometimes providing the one that doesn't exist or a regionalism, which shouldn't be used in specialized texts. Overall, it lacked consistency, which was a downside. Moreover, its developers claim that Lara is context-aware, but sometimes it still had trouble with recognising the meaning of the segment.

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To expand my research, I could contact another MT provider for MateCat in order to obtain the key and see how it handles specialized texts. However, other MTs are concerned with related-language pairs (the most related-language pair I know is English-German but it is not offered) or two varieties of the same language. Perhaps, I could check if it is possible to export translations of specialized texts within the same subject in .tmx format from MateCat and import it to OmegaT. It would create a Translation Memory, which would be a base for other translations of related texts. I wonder if MateCat would be compatible with OmegaT.

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https://padlet.com/nataliapeczynska/natalia-peczy-ska-self-learning-task-7hbqmfglqd9iih65

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Update (13.06.2025):

Lara translated Siamese algae eaters literally. In result, there was a calque (zjadacze glonów syjamskie) in the target text; no sources provide that equivalent of the English term. Moreover, the word order seems unnatural in Polish. As for the translation of crowd together, Lara gave a similar suggestion to Modern MT Lite - (tłoczyć się/gromadzić się).

As it was mentioned, it handled the next segment better in comparsion to the built-in MT (it has the same sense as the original), but chose the word wijun. According to the online dictionary PWN, wijun is a regionalism and means "piskorz". It is a better translation than kózka ssąca, because sucking loaches belong to the fish family of cobitidae (piskorzowate).

As for the third segment, Lara translated stomach in the same way as the built-in MT (żołądek) on its first attempt; however, the second time, it provided the correct translation - brzuch.

Lara handled the last segment in the same way as the built-in MT, creating a ridiculous translation. It used the word węgorz, which doesn't make sense in this context and is confusing.

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Lara vs. Modern MT Lite - units of measurement

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Lara vs. Modern MT Lite - units of measurement

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Modern MT Lite kept the unit of measurement from the source text and translated it as cale, whereas Lara changed it to centimeters. It did it correctly, which is impressive.

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The built-in MT didn't recognize the appropriate term for sucking loach, and therefore created a ridiculous and completely incorrect translation.

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Files (target texts and translation memories)

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Files (target texts and translation memories)