AutoML Translation beginner's guide

AutoML Translation lets you build custom models (without writing code) that are tailored for your domain-specific content compared to the default Google Neural Machine Translation (NMT) model.

Imagine you run a financial reporting service that has an opportunity to expand to new countries. Those markets require that your time-sensitive financial documents are translated in real time. Instead of hiring bilingual finance staff or contracting with a specialist translator, both of which come at a high price due to their domain expertise and your need for quick turnaround, a custom model can help you automate translation jobs in a scalable way.

Note: AutoML Translation capabilities are offered by both the AutoML API and the Cloud Translation - Advanced API (v3). We recommend that you use the Cloud Translation API to create datasets and models because any future enhancements will apply only to the Cloud Translation API. If you have existing AutoML resources, you can upgrade them to work with the Cloud Translation API. For more information, see Upgrade AutoML resources.

Try it for yourself

If you're new to Google Cloud, create an account to evaluate how Cloud Translation performs in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

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Why is Machine Learning (ML) the right tool for this problem?

Classical programming requires the programmer to specify step-by-step instructions for the computer to follow. But this rule-based approach quickly gets unfeasible for translation. Natural language is complex, and translating it is complex too. Machine translation is done almost entirely with a statistical approach, with massive parallel corpora replacing linguistic experts fine-tuning specialized sets of rules by hand.

You need a system that can generalize to a wide variety of translation scenarios but is laser-focused on your use case and task-specific linguistic domain in the language pairs you care about. In a scenario where a sequence of specific rules is bound to expand exponentially, you need a system that can learn from examples. Fortunately, machine learning systems are well-positioned to solve this problem.

Is the default NMT model or a custom model the right tool for me?

The neural machine translation (NMT) model covers a large number of language pairs and does well with general-purpose text. Where a custom model really excels is for the "last mile" between generic translation tasks and specific, niche vocabularies. AutoML Translation starts from the generic NMT model and then tunes the model to fit your training data to get the right translation for domain-specific content that matters to you.

What does machine learning involve?

Machine learning involves using data to train algorithms to achieve a desired outcome. The specifics of the algorithm and training methods change based on the problem space. There are many different subcategories of machine learning, all of which solve different problems and work within different constraints. AutoML Translation enables you to perform supervised learning, which involves training a computer to recognize patterns from translated segment pairs. Using supervised learning, we can train a custom model to translate domain-specific content you care about.

Data Preparation

In order to train a custom model, you supply matching pairs of segments in the source and target languages - that is, pairs of segments that mean the same thing in the language you want to translate from and the language you want to translate to. The closer in meaning your segment pairs are, the better your model will work.

Assess your use case

While putting together the dataset, always start with the use case. You can begin with the following questions:

• What is the outcome you're trying to achieve?
• What kinds of segments do you need to translate to achieve this outcome? Is this a task that the NMT model can do out of the box?
• Is it possible for humans to translate these segments in a way that satisfies you? If the translation task is inherently ambiguous, to the point where a person fluent in both languages would have a hard time doing a satisfactory job, you might find the NMT model and your custom model to be similar in performance.
• What kinds of examples would best reflect the type and range of data your system will need to translate?

A core principle underpinning Google's ML products is human-centered machine learning, an approach that foregrounds responsible AI practices including fairness. The goal of fairness in ML is to understand and prevent unjust or prejudicial treatment of people related to race, income, sexual orientation, religion, gender, and other characteristics historically associated with discrimination and marginalization, when and where they manifest in algorithmic systems or algorithmically aided decision-making. You can read more in our guide and find fair-aware notes ✽ in the guidelines below. As you move through the guidelines for putting together your dataset, we encourage you to consider fairness in machine learning where relevant to your use case.

Fair-aware: Could your use case or product negatively impact individuals' economic or other important life opportunities? If so, read more about assessing your use case for fairness considerations.

Source your data

After you've established what data you need, you need to find a way to source it. You can begin by taking into account all the data your organization collects. You might find that you're already collecting the data you would need to train a translation model. In case you don't have the data you need, you can obtain it manually or outsource it to a third-party provider.

Fair-aware: Review regulations in both your region and the locations your application will serve, as well as existing research or product information in your domain to learn about legal guidelines and common issues. Read more.

Match data to your problem domain

You're training a custom translation model because you need a model that fits a particular linguistic domain. Make sure your segment pairs do the best possible job of covering the vocabulary, usage, and grammatical quirks of your industry or area of focus. Find documents that contain typical usages you'd find in the translation tasks you want accomplished, and make sure your parallel phrases match as closely in meaning as you can arrange. Of course, sometimes languages don't map perfectly in vocabulary or syntax, but try to capture the full diversity of semantics you expect to encounter in use if that's possible. You're building on top of a model that already does a pretty good job with general-purpose translation - your examples are the special last step that makes custom models work for your use case in particular, so make sure they're relevant and representative of usage you expect to see.

Capture the diversity of your linguistic space

It's tempting to assume that the way people write about a specific domain is uniform enough that a small number of text samples translated by a small number of translators should be sufficient to train a model that works well for anyone else writing about that domain. But we're all individuals, and we each bring our own personality to the words we write. A training dataset with segment pairs from a broad selection of authors and translators is more likely to give you a model that's useful for translating writing from a diverse organization. In addition, consider the variety of segment lengths and structures; a dataset where all the segments are the same size or share a similar grammatical structure will not give Cloud Translation enough information to build a good model that captures all the possibilities.

Keep humans in the loop

If it's at all feasible, make sure a person who understands both languages well has validated the segment pairs match up correctly and represent understandable, accurate translations. A mistake as simple as misaligning the rows of your training data spreadsheet can yield translations that sound like nonsense. High-quality data is the most important thing you can provide to AutoML Translation to get a model that's usable for your business.

Clean up messy data

It's easy to make mistakes when preprocessing data, and some of those mistakes can really confuse a custom model. In particular, look for the following data issues that you can fix:

• Remove duplicate source segments, particularly if they have different target translations. AutoML Translation uses only the first seen example and drops all other pairs at import time. By removing duplicates, you ensure AutoML Translation uses your preferred translation.
• Align source segments to the correct target segments.
• Match segments to the specified language; for example, include only Chinese segments in a Chinese dataset.
• For target segments that include mixed languages, check that untranslated words are intentionally untranslated, such as names of products or organizations. Target segments that mistakenly include untranslated words add noise to your training data, which can result in a lower quality model.
• Fix segments with typographical or grammatical errors so that your model doesn't learn these errors.
• Remove non-translatable content such as placeholder tags and HTML tags. Non-translatable content can result in punctuation errors.
• Don't include translations that replace general entities with specific nouns. For instance, you might have an example that changes "president" to a name of a specific president like "JFK" or "John F Kennedy." The model might learn to change all instances of "president" to "JFK." Instead, remove these translations or change the specific nouns to a common one.
• Remove duplicate segments in the training and test sets. (Learn more about train and test sets)
• Split multiple segments into different segment pairs. Training on a dataset where many items have more than about 50 tokens (words) in them yields lower quality models. Split items into individual sentences where possible.
• Use consistent casing. Casing affects how a model learns, for example, to distinguish a headline versus body text.
• Remove TMX tags when importing data from a TSV file. In some cases, you might export your existing translation memory to a TSV file, which might include TMX tags. However, AutoML Translation cleans up translation unit tags only when you import from a TMX file (not for TSV files).

How AutoML Translation preprocesses your data

AutoML Translation stops parsing your data input file when:

• There is invalid formatting
• There is an unreasonably long segment pair (10 MB)
• The file uses an encoding other than UTF-8

AutoML Translation ignores errors for problems it cannot detect, such as:

• A <tu> element in a TMX file doesn't have the source language or target language.
• One of the input segment pairs is empty.

For automatic data splitting, AutoML Translation does additional processing:

• After the dataset is uploaded, it removes segment pairs with identical source segments.
• It randomly splits your data into three sets with a ratio of 8:1:1 (train:validation:test) before training.

Consider how AutoML Translation uses your dataset in creating a custom model

Your dataset contains training, validation and testing sets. If you do not specify the splits (see Preparing your training data and your dataset contains under 100,000 segment pairs, then AutoML Translation automatically uses 80% of your content documents for training, 10% for validating, and 10% for testing. If your data is larger than that, you'll need to perform your own data split.

Training Set

The vast majority of your data should be in the training set. This is the data your model "sees" during training: it's used to learn the parameters of the model, namely the weights of the connections between nodes of the neural network.

Validation Set

The validation set, sometimes also called the "dev" set, is also used during the training process. During model learning, the framework uses the training set to train a suite of candidate models, and then uses the model's performance on the validation set to choose the best model generated. It uses the model's performance on the validation set to tune the model's hyperparameters, which are variables that specify the model's structure. If you used the training set to tune the hyperparameters, the model would end up overly focused on your training data. Using a somewhat novel dataset to fine-tune model structure means your model will generalize better.

Test Set

The test set is not involved in the training process at all. Once the model has completed its training entirely, we use the test set as an entirely new challenge for your model. The performance of your model on the test set is intended to give you a pretty good idea of how your model will perform on real-world data.

Manual Splitting

AutoML Translation can split your data into training, validation, and test sets for you, or you can do it yourself if you want to exercise more control over the process, if you'd prefer a different percentage split, or if there are specific examples that you're sure you want included in a certain part of your model training lifecycle.

 

Fair-aware: If you have scarce data about a particular subgroup, make sure that data is spread representatively between your training and test sets by performing the train/test split yourself. Read more.

Prepare your data for import

After you've decided if a manual or automatic split of your data is right for you, there are two ways to add data:

• You can import data as a tab-separated values (TSV) file containing source and target segments, one segment pair per line.
• You can import data as a TMX file, a standard format for providing segment pairs to automatic translation model tools (learn more about the supported TMX format). If the TMX file contains invalid XML tags, AutoML ignores them. If the TMX file does not conform to proper XML and TMX format – for example, if it is missing an end tag or a <tmx> element – AutoML will not process it. Cloud Translation also ends processing and returns an error if it skips more than 1024 invalid <tu> elements.

Evaluate

After your model is trained, you receive a summary of your model performance. Click the Train tab to view a detailed analysis.

What should I keep in mind before evaluating my model?

Debugging a model is more about debugging the data than the model itself. If your model starts acting in an unexpected manner as you're evaluating its performance before and after pushing to production, you should return and check your data to see where it can be improved.

BLEU score

The BLEU score is a standard way to measure the quality of a machine translation system. AutoML Translation uses a BLEU score calculated on the test data you've provided as its primary evaluation metric. (Learn more about BLEU scores.)

The Google NMT model, which powers the Cloud Translation API, is built for general usage. It might not be the best solution for you if you are looking for specialized translation in your own fields. The trained custom model usually works better than the NMT model in the fields that your training set is related to.

After you train the custom model with your own dataset, the BLEU score of the custom model and Google NMT model are shown in the Train tab. There is also a BLEU score performance gain from the custom model on the Train tab. The higher the BLEU score, the better translations your model can give you for segments that are similar to your training data. If the BLEU score falls in the range 30-40, the model is considered to be able to provide good translations.

Testing your model

Even if the BLEU score looks okay, it's a good practice to check the model yourself to make sure its performance matches your expectations. If your training and test data are drawn from the same incorrect set of samples, the scores might be excellent even if the translation is nonsense. Add some examples as input on the Predict tab and compare the results from the custom model with the Google NMT base model. You might notice that your model comes up with the same predictions as the base model, especially on short segments or if you have a smaller training set. This isn't unexpected - the base model is already pretty good for a wide variety of use cases. Try some longer or more complex segments. However, if all of your segments come back identical to the predictions from the base model, this may indicate a data problem.

Fair-aware: Think carefully about your problem domain and its potential for unfairness and bias. Come up with cases that would adversely impact your users if they were found in production, and test those first. Read more.

If there's a mistake that you're particularly worried about your model making (for example, a confusing feature of your language pair that often trips up human translators, or a translation mistake that might be especially costly in money or reputation) make sure your test set or procedure covers that case adequately for you to feel safe using your model in everyday tasks.

Fair-aware: If you have a use case that warrants fairness considerations, read more about how to use your model in a manner that mitigates biases or adverse outcomes. Read more.

What's next

• To create your own dataset and custom model, see Prepare training data for instructions on how to prepare your data.