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Detect NLP data drift using custom Amazon SageMaker Model Monitor

Natural language understanding is applied in a wide range of use cases, from chatbots and virtual assistants, to machine translation and text summarization. To ensure that these applications are running at an expected level of performance, it’s important that data in the training and production environments is from the same distribution. When the data that…

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[]Natural language understanding is applied in a wide range of use cases, from chatbots and virtual assistants, to machine translation and text summarization. To ensure that these applications are running at an expected level of performance, it’s important that data in the training and production environments is from the same distribution. When the data that is used for inference (production data) differs from the data used during model training, we encounter a phenomenon known as data drift. When data drift occurs, the model is no longer relevant to the data in production and likely performs worse than expected. It’s important to continuously monitor the inference data and compare it to the data used during training.

[]You can use Amazon SageMaker to quickly build, train, and deploy machine learning (ML) models at any scale. As a proactive measure against model degradation, you can use Amazon SageMaker Model Monitor to continuously monitor the quality of your ML models in real time. With Model Monitor, you can also configure alerts to notify and trigger actions if any drift in model performance is observed. Early and proactive detection of these deviations enables you to take corrective actions, such as collecting new ground truth training data, retraining models, and auditing upstream systems, without having to manually monitor models or build additional tooling.

[]Model Monitor offers four different types of monitoring capabilities to detect and mitigate model drift in real time:

  • Data quality – Helps detect change in data schemas and statistical properties of independent variables and alerts when a drift is detected.
  • Model quality – For monitoring model performance characteristics such as accuracy or precision in real time, Model Monitor allows you to ingest the ground truth labels collected from your applications. Model Monitor automatically merges the ground truth information with prediction data to compute the model performance metrics.
  • Model bias –Model Monitor is integrated with Amazon SageMaker Clarify to improve visibility into potential bias. Although your initial data or model may not be biased, changes in the world may cause bias to develop over time in a model that has already been trained.
  • Model explainability – Drift detection alerts you when a change occurs in the relative importance of feature attributions.

[]In this post, we discuss the types of data quality drift that are applicable to text data. We also present an approach to detecting data drift in text data using Model Monitor.

Data drift in NLP

[]Data drift can be classified into three categories depending on whether the distribution shift is happening on the input or on the output side, or whether the relationship between the input and the output has changed.

Covariate shift

[]In a covariate shift, the distribution of inputs changes over time, but the conditional distribution P(y|x) doesn’t change. This type of drift is called covariate shift because the problem arises due to a shift in the distribution of the covariates (features). For example, in an email spam classification model, distribution of training data (email corpora) may diverge from the distribution of data during scoring.

Label shift

[]While covariate shift focuses on changes in the feature distribution, label shift focuses on changes in the distribution of the class variable. This type of shifting is essentially the reverse of covariate shift. An intuitive way to think about it might be to consider an unbalanced dataset. If the spam to non-spam ratio of emails in our training set is 50%, but in reality 10% of our emails are non-spam, then the target label distribution has shifted.

Concept shift

[]Concept shift is different from covariate and label shift in that it’s not related to the data distribution or the class distribution, but instead is related to the relationship between the two variables. For example, email spammers often use a variety of concepts to pass the spam filter models, and the concept of emails used during training may change as time goes by.

[]Now that we understand the different types of data drift, let’s see how we can use Model Monitor to detect covariate shift in text data.

Solution overview

[]Unlike tabular data, which is structured and bounded, textual data is complex, high dimensional, and free form. To efficiently detect drift in NLP, we work with embeddings, which are low-dimensional representations of the text. You can obtain embeddings using various language models such as Word2Vec and transformer-based models like BERT. These models project high-dimensional data into low-dimensional spaces while preserving the semantic information of the text. The results are dense and contextually meaningful vectors, which can be used for various downstream tasks, including monitoring for data drift.

[]In our solution, we use embeddings to detect the covariate shift of English sentences. We utilize Model Monitor to facilitate continuous monitoring for a text classifier that is deployed to a production environment. Our approach consists of the following steps:

  1. Fine-tune a BERT model using SageMaker.
  2. Deploy a fine-tuned BERT classifier as a real-time endpoint with data capture enabled.
  3. Create a baseline dataset that consists of a sample of the sentences used to train the BERT classifier.
  4. Create a custom SageMaker monitoring job to calculate the cosine similarity between the data captured in production and the baseline dataset.

[]The following diagram illustrates the solution workflow:

[]

Fine-tune a BERT model

[]In this post, we use Corpus of Linguistic Acceptability (CoLA), a dataset of 10,657 English sentences labeled as grammatical or ungrammatical from published linguistics literature. We use SageMaker training to fine-tune a BERT model using the CoLa dataset by defining an PyTorch estimator class. For more information on how to use this SDK with PyTorch, see Use PyTorch with the SageMaker Python SDK. Calling the fit() method of the estimator launches the training job:

from sagemaker.pytorch import PyTorch # place to save model artifact output_path = f”s3://{bucket}/{model_prefix}” estimator = PyTorch( entry_point=”train_deploy.py”, source_dir=”code”, role=role, framework_version=”1.7.1″, py_version=”py3″, instance_count=1, instance_type=”ml.p3.2xlarge”, output_path=output_path, hyperparameters={ “epochs”: 1, “num_labels”: 2, “backend”: “gloo”, }, disable_profiler=True, # disable debugger ) estimator.fit({“training”: inputs_train, “testing”: inputs_test})

Deploy the model

[]After training our model, we host it on a SageMaker endpoint. To make the endpoint load the model and serve predictions, we implement a few methods in train_deploy.py:

  • model_fn() – Loads the saved model and returns a model object that can be used for model serving. The SageMaker PyTorch model server loads our model by invoking model_fn.
  • input_fn() – Deserializes and prepares the prediction input. In this example, our request body is first serialized to JSON and then sent to the model serving endpoint. Therefore, in input_fn(), we first deserialize the JSON-formatted request body and return the input as a torch.tensor, as required for BERT.
  • predict_fn() – Performs the prediction and returns the result.

Enable Model Monitor data capture

[]We enable Model Monitor data capture to record the input data into the Amazon Simple Storage Service (Amazon S3) bucket to reference it later:

data_capture_config = DataCaptureConfig(enable_capture=True, sampling_percentage=100, destination_s3_uri=s3_capture_upload_path) []Then we create a real-time SageMaker endpoint with the model created in the previous step:

predictor = estimator.deploy(endpoint_name=’nlp-data-drift-bert-endpoint’, initial_instance_count=1, instance_type=”ml.m4.xlarge”, data_capture_config=data_capture_config)

Inference

[]We run prediction using the predictor object that we created in the previous step. We set JSON serializer and deserializer, which is used by the inference endpoint:

print(“Sending test traffic to the endpoint {}. nPlease wait…”.format(endpoint_name)) result = predictor.predict([ “Thanks so much for driving me home”, “Thanks so much for cooking dinner. I really appreciate it”, “Nice to meet you, Sergio. So, where are you from” ]) []The real-time endpoint is configured to capture data from the request, and the response and the data gets stored in Amazon S3. You can view the data that’s captured in the previous monitoring schedule.

Create a baseline

[]We use a fine-tuned BERT model to extract sentence embedding features from the training data. We use these vectors as high-quality feature inputs for comparing cosine distance because BERT produces dynamic word representation with semantic context. Complete the following steps to get sentence embedding:

  1. Use a BERT tokenizer to get token IDs for each token (input_id) in the input sentence and mask to indicate which elements in the input sequence are tokens vs. padding elements (attention_mask_id). We use the BERT tokenizer.encode_plus function to get these values for each input sentence:

#Add instantiation of tokenizer encoded_dict = tokenizer.encode_plus( sent, # Input Sentence to encode. add_special_tokens = True, # Add ‘[CLS]’ and ‘[SEP]’ max_length = 64, # Pad sentence to max_length pad_to_max_length = True, # Truncate sentence to max_length return_attention_mask = True, #BERT model needs attention_mask return_tensors = ‘pt’, # Return pytorch tensors. ) input_ids = encoded_dict[‘input_ids’] attention_mask_ids = encoded_dict[‘attention_mask’] []input_ids and attention_mask_ids are passed to the model and fetch the hidden states of the network. The hidden_states has four dimensions in the following order:

  • Layer number (BERT has 12 layers)
  • Batch number (1 sentence)
  • Word token indexes
  • Hidden units (768 features)
  1. Use the last two hidden layers to get a single vector (sentence embedding) by calculating the average of all input tokens in the sentence:

outputs = model(input_ids, attention_mask_ids) # forward pass to model hidden_states = outputs[2] # token vectors token_vecs = hidden_states[-2][0] # last 2 layer hidden states sentence_embedding = torch.mean(token_vecs, dim=0) # average token vectors

  1. Convert the sentence embedding as a NumPy array and store it in an Amazon S3 location as a baseline that is used by Model Monitor:

sentence_embeddings_list = []for i in sentence_embeddings:sentence_embeddings_list.append(i.numpy()) np.save(’embeddings.npy’, sentence_embeddings_list) #Upload the sentence embedding to S3 !aws s3 cp embeddings.npy s3://{bucket}/{model_prefix}/embeddings/

Evaluation script

[]Model Monitor provides a pre-built container with the ability to analyze the data captured from endpoints for tabular datasets. If you want to bring your own container, Model Monitor provides extension points that you can use. When you create a MonitoringSchedule, Model Monitor ultimately kicks off processing jobs. Therefore, the container needs to be aware of the processing job contract. We need to create an evaluation script that is compatible with container contract inputs and outputs.

[]Model Monitor uses evaluation code on all the samples that are captured during the monitoring schedule. For each inference data point, we calculate the sentence embedding using the same logic described earlier. Cosine similarity is used as a distance metric to measure the similarity of an inference data point and sentence embeddings in the baseline. Mathematically, it measures the cosine angle between two sentence embedding vectors. A high the cosine similarity score indicates similar sentence embeddings. A lower cosine similarity score indicates data drift. We calculate an average of all the cosine similarity scores, and if it’s less than the threshold, it gets captured in the violation report. Based on the use case, you can use other distance metrics like manhattan or euclidean to measure similarity of sentence embeddings.

[]The following diagram shows how we use SageMaker Model Monitoring to establish baseline and detect data drift using cosine distance similarity.

[]

[]The following is the code for calculating the violations; the complete evaluation script is available on GitHub:

for embed_item in embedding_list: # all sentence embeddings from baseline cosine_score += (1 – cosine(input_sentence_embedding, embed_item)) # cosine distance between input sentence embedding and baseline embedding cosine_score_avg = cosine_score/(len(embedding_list)) # average cosine score of input sentence if cosine_score_avg < env.max_ratio_threshold: # compare averge cosine score against a threshold sent_cosine_dict[record] = cosine_score_avg # capture details for violation report violations.append({ "sentence": record, "avg_cosine_score": cosine_score_avg, "feature_name": "sent_cosine_score", "constraint_check_type": "baseline_drift_check", "endpoint_name" : env.sagemaker_endpoint_name, "monitoring_schedule_name": env.sagemaker_monitoring_schedule_name })

Measure data drift using Model Monitor

[]In this section, we focus on measuring data drift using Model Monitor. Model Monitor pre-built monitors are powered by Deequ, which is a library built on top of Apache Spark for defining unit tests for data, which measure data quality in large datasets. You don’t require coding to utilize these pre-built monitoring capabilities. You also have the flexibility to monitor models by coding to provide custom analysis. You can collect and review all metrics emitted by Model Monitor in Amazon SageMaker Studio, so you can visually analyze your model performance without writing additional code.

[]In certain scenarios, for instance when the data is non-tabular, the default processing job (powered by Deequ) doesn’t suffice because it only supports tabular datasets. The pre-built monitors may not be sufficient to generate sophisticated metrics to detect drifts, and may necessitate bringing your own metrics. In the next sections, we describe the setup to bring in your metrics by building a custom container.

Build the custom Model Monitor container

[]We use the evaluation script from the previous section to build a Docker container and push it to Amazon Elastic Container Registry (Amazon ECR):

#Build a docker container and push to ECR account_id = boto3.client(‘sts’).get_caller_identity().get(‘Account’) ecr_repository = ‘nlp-data-drift-bert-v1’ tag = ‘:latest’ region = boto3.session.Session().region_name sm = boto3.client(‘sagemaker’) uri_suffix = ‘amazonaws.com’ if region in [‘cn-north-1’, ‘cn-northwest-1’]: uri_suffix = ‘amazonaws.com.cn’ processing_repository_uri = f'{account_id}.dkr.ecr.{region}.{uri_suffix}/{ecr_repository + tag}’ # Creating the ECR repository and pushing the container image !docker build -t $ecr_repository docker !$(aws ecr get-login –region $region –registry-ids $account_id –no-include-email) !aws ecr create-repository –repository-name $ecr_repository !docker tag {ecr_repository + tag} $processing_repository_uri!docker push $processing_repository_uri []When the customer Docker container is in Amazon ECR, we can schedule a Model Monitoring job and generate a violations report, as demonstrated in the next sections.

Schedule a model monitoring job

[]To schedule a model monitoring job, we create an instance of Model Monitor and in the image_uri, we refer to the Docker container that we created in the previous section:

from sagemaker.model_monitor import ModelMonitor monitor = ModelMonitor( base_job_name=’nlp-data-drift-bert-v1′, role=role, image_uri=processing_repository_uri, instance_count=1, instance_type=’ml.m5.large’, env={ ‘THRESHOLD’:’0.5′, ‘bucket’: bucket }, ) []We schedule the monitoring job using the create_monitoring_schedule API. You can schedule the monitoring job on an hourly or daily basis. You configure the job using the destination parameter, as shown in the following code:

from sagemaker.model_monitor import CronExpressionGenerator, MonitoringOutput from sagemaker.processing import ProcessingInput, ProcessingOutput destination = f’s3://{sagemaker_session.default_bucket()}/{prefix}/{endpoint_name}/monitoring_schedule’ processing_output = ProcessingOutput( output_name=’result’, source=’/opt/ml/processing/resultdata’, destination=destination, ) output = MonitoringOutput(source=processing_output.source, destination=processing_output.destination) monitor.create_monitoring_schedule( monitor_schedule_name=’nlp-data-drift-bert-schedule’, output=output, endpoint_input=predictor.endpoint_name, schedule_cron_expression=CronExpressionGenerator.hourly(), ) []To describe and list the monitoring schedule and its runs, you can use the following commands:

monitor.describe_schedule() print(monitor.list_executions())

Data drift violation report

[]When the model monitoring job is complete, you can navigate to the destination S3 path to access the violation reports. This report contains all the inputs whose average cosine score (avg_cosine_score) is below the threshold configured as an environment variable THRESHOLD:0.5 in the ModelMonitor instance. This is an indication that the data observed during inference is drifting beyond the established baseline.

[]The following code shows the generated violation report:

{ “violations”: [ { “feature_name”: “sent_cosine_score”, “constraint_check_type”: “baseline_drift_check”, “sentence”: “Thanks so much for driving me home”, “avg_cosine_score”: 0.36653404209142876 }, { “feature_name”: “sent_cosine_score”, “constraint_check_type”: “baseline_drift_check”, “sentence”: “Thanks so much for cooking dinner. I really appreciate it”, “avg_cosine_score”: 0.34974955975723576 }, { “feature_name”: “sent_cosine_score”, “constraint_check_type”: “baseline_drift_check”, “sentence”: “Nice to meet you, Sergio. So, where are you from”, “avg_cosine_score”: 0.378982806084463 } ] } []Finally, based on this observation, you can configure your model for retraining. You can also enable Amazon Simple Notification Service (Amazon SNS) notifications to send alerts when violations occur.

Conclusion

[]Model Monitor enables you to maintain the high quality of your models in production. In this post, we highlighted the challenges with monitoring data drift on unstructured data like text, and provided an intuitive approach to detect data drift using a custom monitoring script. You can find the code associated with the post in the following GitHub repository. Additionally, you can customize the solution to utilize other distance metrics such as maximum mean discrepancy (MMD), a non-parametric distance metric to compute marginal distribution between source and target distribution on the embedded space.

About the Authors

[]Vikram Elango is an AI/ML Specialist Solutions Architect at Amazon Web Services, based in Virginia, USA. Vikram helps financial and insurance industry customers with design, thought leadership to build and deploy machine learning applications at scale. He is currently focused on natural language processing, responsible AI, inference optimization and scaling ML across the enterprise. In his spare time, he enjoys traveling, hiking, cooking and camping with his family.

[]Raghu Ramesha is a ML Solutions Architect with the Amazon SageMaker Service team. He focuses on helping customers migrate ML production workloads to SageMaker at scale. He specializes in machine learning, AI, and computer vision domains, and holds a master’s degree in Computer Science from UT Dallas. In his free time, he enjoys traveling and photography.

[]Tony Chen is a Machine Learning Solutions Architect at Amazon Web Services, helping customers design scalable and robust machine learning capabilities in the cloud. As a former data scientist and data engineer, he leverages his experience to help tackle some of the most challenging problems organizations face with operationalizing machine learning.



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Customize pronunciation using lexicons in Amazon Polly

Amazon Polly is a text-to-speech service that uses advanced deep learning technologies to synthesize natural-sounding human speech. It is used in a variety of use cases, such as contact center systems, delivering conversational user experiences with human-like voices for automated real-time status check, automated account and billing inquiries, and by news agencies like The Washington…

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Amazon Polly is a text-to-speech service that uses advanced deep learning technologies to synthesize natural-sounding human speech. It is used in a variety of use cases, such as contact center systems, delivering conversational user experiences with human-like voices for automated real-time status check, automated account and billing inquiries, and by news agencies like The Washington Post to allow readers to listen to news articles.

As of today, Amazon Polly provides over 60 voices in 30+ language variants. Amazon Polly also uses context to pronounce certain words differently based upon the verb tense and other contextual information. For example, “read” in “I read a book” (present tense) and “I will read a book” (future tense) is pronounced differently.

However, in some situations you may want to customize the way Amazon Polly pronounces a word. For example, you may need to match the pronunciation with local dialect or vernacular. Names of things (e.g., Tomato can be pronounced as tom-ah-to or tom-ay-to), people, streets, or places are often pronounced in many different ways.

In this post, we demonstrate how you can leverage lexicons for creating custom pronunciations. You can apply lexicons for use cases such as publishing, education, or call centers.

Customize pronunciation using SSML tag

Let’s say you stream a popular podcast from Australia and you use the Amazon Polly Australian English (Olivia) voice to convert your script into human-like speech. In one of your scripts, you want to use words that are unknown to Amazon Polly voice. For example, you want to send Mātariki (Māori New Year) greetings to your New Zealand listeners. For such scenarios, Amazon Polly supports phonetic pronunciation, which you can use to achieve a pronunciation that is close to the correct pronunciation in the foreign language.

You can use the Speech Synthesis Markup Language (SSML) tag to suggest a phonetic pronunciation in the ph attribute. Let me show you how you can use SSML tag.

First, login into your AWS console and search for Amazon Polly in the search bar at the top. Select Amazon Polly and then choose Try Polly button.

In the Amazon Polly console, select Australian English from the language dropdown and enter following text in the Input text box and then click on Listen to test the pronunciation.

I’m wishing you all a very Happy Mātariki.

Sample speech without applying phonetic pronunciation:

If you hear the sample speech above, you can notice that the pronunciation of Mātariki – a word which is not part of Australian English – isn’t quite spot-on. Now, let’s look at how in such scenarios we can use phonetic pronunciation using SSML tag to customize the speech produced by Amazon Polly.

To use SSML tags, turn ON the SSML option in Amazon Polly console. Then copy and paste following SSML script containing phonetic pronunciation for Mātariki specified inside the ph attribute of the tag.

I’m wishing you all a very Happy Mātariki.

With the tag, Amazon Polly uses the pronunciation specified by the ph attribute instead of the standard pronunciation associated by default with the language used by the selected voice.

Sample speech after applying phonetic pronunciation:

If you hear the sample sound, you’ll notice that we opted for a different pronunciation for some of vowels (e.g., ā) to make Amazon Polly synthesize the sounds that are closer to the correct pronunciation. Now you might have a question, how do I generate the phonetic transcription “mA:.tA:.ri.ki” for the word Mātariki?

You can create phonetic transcriptions by referring to the Phoneme and Viseme tables for the supported languages. In the example above we have used the phonemes for Australian English.

Amazon Polly offers support in two phonetic alphabets: IPA and X-Sampa. Benefit of X-Sampa is that they are standard ASCII characters, so it is easier to type the phonetic transcription with a normal keyboard. You can use either of IPA or X-Sampa to generate your transcriptions, but make sure to stay consistent with your choice, especially when you use a lexicon file which we’ll cover in the next section.

Each phoneme in the phoneme table represents a speech sound. The bolded letters in the “Example” column of the Phoneme/Viseme table in the Australian English page linked above represent the part of the word the “Phoneme” corresponds to. For example, the phoneme /j/ represents the sound that an Australian English speaker makes when pronouncing the letter “y” in “yes.”

Customize pronunciation using lexicons

Phoneme tags are suitable for one-off situations to customize isolated cases, but these are not scalable. If you process huge volume of text, managed by different editors and reviewers, we recommend using lexicons. Using lexicons, you can achieve consistency in adding custom pronunciations and simultaneously reduce manual effort of inserting phoneme tags into the script.

A good practice is that after you test the custom pronunciation on the Amazon Polly console using the tag, you create a library of customized pronunciations using lexicons. Once lexicons file is uploaded, Amazon Polly will automatically apply phonetic pronunciations specified in the lexicons file and eliminate the need to manually provide a tag.

Create a lexicon file

A lexicon file contains the mapping between words and their phonetic pronunciations. Pronunciation Lexicon Specification (PLS) is a W3C recommendation for specifying interoperable pronunciation information. The following is an example PLS document:

Matariki Mātariki mA:.tA:.ri.ki NZ New Zealand

Make sure that you use correct value for the xml:lang field. Use en-AU if you’re uploading the lexicon file to use with the Amazon Polly Australian English voice. For a complete list of supported languages, refer to Languages Supported by Amazon Polly.

To specify a custom pronunciation, you need to add a element which is a container for a lexical entry with one or more element and one or more pronunciation information provided inside element.

The element contains the text describing the orthography of the element. You can use a element to specify the word whose pronunciation you want to customize. You can add multiple elements to specify all word variations, for example with or without macrons. The element is case-sensitive, and during speech synthesis Amazon Polly string matches the words inside your script that you’re converting to speech. If a match is found, it uses the element, which describes how the is pronounced to generate phonetic transcription.

You can also use for commonly used abbreviations. In the preceding example of a lexicon file, NZ is used as an alias for New Zealand. This means that whenever Amazon Polly comes across “NZ” (with matching case) in the body of the text, it’ll read those two letters as “New Zealand”.

For more information on lexicon file format, see Pronunciation Lexicon Specification (PLS) Version 1.0 on the W3C website.

You can save a lexicon file with as a .pls or .xml file before uploading it to Amazon Polly.

Upload and apply the lexicon file

Upload your lexicon file to Amazon Polly using the following instructions:

  1. On the Amazon Polly console, choose Lexicons in the navigation pane.
  2. Choose Upload lexicon.
  3. Enter a name for the lexicon and then choose a lexicon file.
  4. Choose the file to upload.
  5. Choose Upload lexicon.

If a lexicon by the same name (whether a .pls or .xml file) already exists, uploading the lexicon overwrites the existing lexicon.

Now you can apply the lexicon to customize pronunciation.

  1. Choose Text-to-Speech in the navigation pane.
  2. Expand Additional settings.
  3. Turn on Customize pronunciation.
  4. Choose the lexicon on the drop-down menu.

You can also choose Upload lexicon to upload a new lexicon file (or a new version).

It’s a good practice to version control the lexicon file in a source code repository. Keeping the custom pronunciations in a lexicon file ensures that you can consistently refer to phonetic pronunciations for certain words across the organization. Also, keep in mind the pronunciation lexicon limits mentioned on Quotas in Amazon Polly page.

Test the pronunciation after applying the lexicon

Let’s perform quick test using “Wishing all my listeners in NZ, a very Happy Mātariki” as the input text.

We can compare the audio files before and after applying the lexicon.

Before applying the lexicon:

After applying the lexicon:

Conclusion

In this post, we discussed how you can customize pronunciations of commonly used acronyms or words not found in the selected language in Amazon Polly. You can use SSML tag which is great for inserting one-off customizations or testing purposes. We recommend using Lexicon to create a consistent set of pronunciations for frequently used words across your organization. This enables your content writers to spend time on writing instead of the tedious task of adding phonetic pronunciations in the script repetitively. You can try this in your AWS account on the Amazon Polly console.

Summary of resources

About the Authors

Ratan Kumar is a Solutions Architect based out of Auckland, New Zealand. He works with large enterprise customers helping them design and build secure, cost-effective, and reliable internet scale applications using the AWS cloud. He is passionate about technology and likes sharing knowledge through blog posts and twitch sessions.

Maciek Tegi is a Principal Audio Designer and a Product Manager for Polly Brand Voices. He has worked in professional capacity in the tech industry, movies, commercials and game localization. In 2013, he was the first audio engineer hired to the Alexa Text-To- Speech team. Maciek was involved in releasing 12 Alexa TTS voices across different countries, over 20 Polly voices, and 4 Alexa celebrity voices. Maciek is a triathlete, and an avid acoustic guitar player.



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AWS Week in Review – May 16, 2022

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS! I had been on the road for the last five weeks and attended many of the AWS Summits in Europe. It was great to talk to so many of you…

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This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

I had been on the road for the last five weeks and attended many of the AWS Summits in Europe. It was great to talk to so many of you in person. The Serverless Developer Advocates are going around many of the AWS Summits with the Serverlesspresso booth. If you attend an event that has the booth, say “Hi ” to my colleagues, and have a coffee while asking all your serverless questions. You can find all the upcoming AWS Summits in the events section at the end of this post.

Last week’s launches
Here are some launches that got my attention during the previous week.

AWS Step Functions announced a new console experience to debug your state machine executions – Now you can opt-in to the new console experience of Step Functions, which makes it easier to analyze, debug, and optimize Standard Workflows. The new page allows you to inspect executions using three different views: graph, table, and event view, and add many new features to enhance the navigation and analysis of the executions. To learn about all the features and how to use them, read Ben’s blog post.

Example on how the Graph View looks

Example on how the Graph View looks

AWS Lambda now supports Node.js 16.x runtime – Now you can start using the Node.js 16 runtime when you create a new function or update your existing functions to use it. You can also use the new container image base that supports this runtime. To learn more about this launch, check Dan’s blog post.

AWS Amplify announces its Android library designed for Kotlin – The Amplify Android library has been rewritten for Kotlin, and now it is available in preview. This new library provides better debugging capacities and visibility into underlying state management. And it is also using the new AWS SDK for Kotlin that was released last year in preview. Read the What’s New post for more information.

Three new APIs for batch data retrieval in AWS IoT SiteWise – With this new launch AWS IoT SiteWise now supports batch data retrieval from multiple asset properties. The new APIs allow you to retrieve current values, historical values, and aggregated values. Read the What’s New post to learn how you can start using the new APIs.

AWS Secrets Manager now publishes secret usage metrics to Amazon CloudWatch – This launch is very useful to see the number of secrets in your account and set alarms for any unexpected increase or decrease in the number of secrets. Read the documentation on Monitoring Secrets Manager with Amazon CloudWatch for more information.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Some other launches and news that you may have missed:

IBM signed a deal with AWS to offer its software portfolio as a service on AWS. This allows customers using AWS to access IBM software for automation, data and artificial intelligence, and security that is built on Red Hat OpenShift Service on AWS.

Podcast Charlas Técnicas de AWS – If you understand Spanish, this podcast is for you. Podcast Charlas Técnicas is one of the official AWS podcasts in Spanish. This week’s episode introduces you to Amazon DynamoDB and shares stories on how different customers use this database service. You can listen to all the episodes directly from your favorite podcast app or the podcast web page.

AWS Open Source News and Updates – Ricardo Sueiras, my colleague from the AWS Developer Relation team, runs this newsletter. It brings you all the latest open-source projects, posts, and more. Read edition #112 here.

Upcoming AWS Events
It’s AWS Summits season and here are some virtual and in-person events that might be close to you:

You can register for re:MARS to get fresh ideas on topics such as machine learning, automation, robotics, and space. The conference will be in person in Las Vegas, June 21–24.

That’s all for this week. Check back next Monday for another Week in Review!

— Marcia



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Personalize your machine translation results by using fuzzy matching with Amazon Translate

A person’s vernacular is part of the characteristics that make them unique. There are often countless different ways to express one specific idea. When a firm communicates with their customers, it’s critical that the message is delivered in a way that best represents the information they’re trying to convey. This becomes even more important when…

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A person’s vernacular is part of the characteristics that make them unique. There are often countless different ways to express one specific idea. When a firm communicates with their customers, it’s critical that the message is delivered in a way that best represents the information they’re trying to convey. This becomes even more important when it comes to professional language translation. Customers of translation systems and services expect accurate and highly customized outputs. To achieve this, they often reuse previous translation outputs—called translation memory (TM)—and compare them to new input text. In computer-assisted translation, this technique is known as fuzzy matching. The primary function of fuzzy matching is to assist the translator by speeding up the translation process. When an exact match can’t be found in the TM database for the text being translated, translation management systems (TMSs) often have the option to search for a match that is less than exact. Potential matches are provided to the translator as additional input for final translation. Translators who enhance their workflow with machine translation capabilities such as Amazon Translate often expect fuzzy matching data to be used as part of the automated translation solution.

In this post, you learn how to customize output from Amazon Translate according to translation memory fuzzy match quality scores.

Translation Quality Match

The XML Localization Interchange File Format (XLIFF) standard is often used as a data exchange format between TMSs and Amazon Translate. XLIFF files produced by TMSs include source and target text data along with match quality scores based on the available TM. These scores—usually expressed as a percentage—indicate how close the translation memory is to the text being translated.

Some customers with very strict requirements only want machine translation to be used when match quality scores are below a certain threshold. Beyond this threshold, they expect their own translation memory to take precedence. Translators often need to apply these preferences manually either within their TMS or by altering the text data. This flow is illustrated in the following diagram. The machine translation system processes the translation data—text and fuzzy match scores— which is then reviewed and manually edited by translators, based on their desired quality thresholds. Applying thresholds as part of the machine translation step allows you to remove these manual steps, which improves efficiency and optimizes cost.

Machine Translation Review Flow

Figure 1: Machine Translation Review Flow

The solution presented in this post allows you to enforce rules based on match quality score thresholds to drive whether a given input text should be machine translated by Amazon Translate or not. When not machine translated, the resulting text is left to the discretion of the translators reviewing the final output.

Solution Architecture

The solution architecture illustrated in Figure 2 leverages the following services:

  • Amazon Simple Storage Service – Amazon S3 buckets contain the following content:
    • Fuzzy match threshold configuration files
    • Source text to be translated
    • Amazon Translate input and output data locations
  • AWS Systems Manager – We use Parameter Store parameters to store match quality threshold configuration values
  • AWS Lambda – We use two Lambda functions:
    • One function preprocesses the quality match threshold configuration files and persists the data into Parameter Store
    • One function automatically creates the asynchronous translation jobs
  • Amazon Simple Queue Service – An Amazon SQS queue triggers the translation flow as a result of new files coming into the source bucket

Solution Architecture Diagram

Figure 2: Solution Architecture

You first set up quality thresholds for your translation jobs by editing a configuration file and uploading it into the fuzzy match threshold configuration S3 bucket. The following is a sample configuration in CSV format. We chose CSV for simplicity, although you can use any format. Each line represents a threshold to be applied to either a specific translation job or as a default value to any job.

default, 75 SourceMT-Test, 80

The specifications of the configuration file are as follows:

  • Column 1 should be populated with the name of the XLIFF file—without extension—provided to the Amazon Translate job as input data.
  • Column 2 should be populated with the quality match percentage threshold. For any score below this value, machine translation is used.
  • For all XLIFF files whose name doesn’t match any name listed in the configuration file, the default threshold is used—the line with the keyword default set in Column 1.

Auto-generated parameter in Systems Manager Parameter Store

Figure 3: Auto-generated parameter in Systems Manager Parameter Store

When a new file is uploaded, Amazon S3 triggers the Lambda function in charge of processing the parameters. This function reads and stores the threshold parameters into Parameter Store for future usage. Using Parameter Store avoids performing redundant Amazon S3 GET requests each time a new translation job is initiated. The sample configuration file produces the parameter tags shown in the following screenshot.

The job initialization Lambda function uses these parameters to preprocess the data prior to invoking Amazon Translate. We use an English-to-Spanish translation XLIFF input file, as shown in the following code. It contains the initial text to be translated, broken down into what is referred to as segments, represented in the source tags.

Consent Form CONSENT FORM FORMULARIO DE CONSENTIMIENTO Screening Visit: Screening Visit Selección

The source text has been pre-matched with the translation memory beforehand. The data contains potential translation alternatives—represented as tags—alongside a match quality attribute, expressed as a percentage. The business rule is as follows:

  • Segments received with alternative translations and a match quality below the threshold are untouched or empty. This signals to Amazon Translate that they must be translated.
  • Segments received with alternative translations with a match quality above the threshold are pre-populated with the suggested target text. Amazon Translate skips those segments.

Let’s assume the quality match threshold configured for this job is 80%. The first segment with 99% match quality isn’t machine translated, whereas the second segment is, because its match quality is below the defined threshold. In this configuration, Amazon Translate produces the following output:

Consent Form FORMULARIO DE CONSENTIMIENTO CONSENT FORM FORMULARIO DE CONSENTIMIENTO Screening Visit: Visita de selección Screening Visit Selección

In the second segment, Amazon Translate overwrites the target text initially suggested (Selección) with a higher quality translation: Visita de selección.

One possible extension to this use case could be to reuse the translated output and create our own translation memory. Amazon Translate supports customization of machine translation using translation memory thanks to the parallel data feature. Text segments previously machine translated due to their initial low-quality score could then be reused in new translation projects.

In the following sections, we walk you through the process of deploying and testing this solution. You use AWS CloudFormation scripts and data samples to launch an asynchronous translation job personalized with a configurable quality match threshold.

Prerequisites

For this walkthrough, you must have an AWS account. If you don’t have an account yet, you can create and activate one.

Launch AWS CloudFormation stack

  1. Choose Launch Stack:
  2. For Stack name, enter a name.
  3. For ConfigBucketName, enter the S3 bucket containing the threshold configuration files.
  4. For ParameterStoreRoot, enter the root path of the parameters created by the parameters processing Lambda function.
  5. For QueueName, enter the SQS queue that you create to post new file notifications from the source bucket to the job initialization Lambda function. This is the function that reads the configuration file.
  6. For SourceBucketName, enter the S3 bucket containing the XLIFF files to be translated. If you prefer to use a preexisting bucket, you need to change the value of the CreateSourceBucket parameter to No.
  7. For WorkingBucketName, enter the S3 bucket Amazon Translate uses for input and output data.
  8. Choose Next.

    Figure 4: CloudFormation stack details

  9. Optionally on the Stack Options page, add key names and values for the tags you may want to assign to the resources about to be created.
  10. Choose Next.
  11. On the Review page, select I acknowledge that this template might cause AWS CloudFormation to create IAM resources.
  12. Review the other settings, then choose Create stack.

AWS CloudFormation takes several minutes to create the resources on your behalf. You can watch the progress on the Events tab on the AWS CloudFormation console. When the stack has been created, you can see a CREATE_COMPLETE message in the Status column on the Overview tab.

Test the solution

Let’s go through a simple example.

  1. Download the following sample data.
  2. Unzip the content.

There should be two files: an .xlf file in XLIFF format, and a threshold configuration file with .cfg as the extension. The following is an excerpt of the XLIFF file.

English to French sample file extract

Figure 5: English to French sample file extract

  1. On the Amazon S3 console, upload the quality threshold configuration file into the configuration bucket you specified earlier.

The value set for test_En_to_Fr is 75%. You should be able to see the parameters on the Systems Manager console in the Parameter Store section.

  1. Still on the Amazon S3 console, upload the .xlf file into the S3 bucket you configured as source. Make sure the file is under a folder named translate (for example, /translate/test_En_to_Fr.xlf).

This starts the translation flow.

  1. Open the Amazon Translate console.

A new job should appear with a status of In Progress.

Auto-generated parameter in Systems Manager Parameter Store

Figure 6: In progress translation jobs on Amazon Translate console

  1. Once the job is complete, click into the job’s link and consult the output. All segments should have been translated.

All segments should have been translated. In the translated XLIFF file, look for segments with additional attributes named lscustom:match-quality, as shown in the following screenshot. These custom attributes identify segments where suggested translation was retained based on score.

Custom attributes identifying segments where suggested translation was retained based on score

Figure 7: Custom attributes identifying segments where suggested translation was retained based on score

These were derived from the translation memory according to the quality threshold. All other segments were machine translated.

You have now deployed and tested an automated asynchronous translation job assistant that enforces configurable translation memory match quality thresholds. Great job!

Cleanup

If you deployed the solution into your account, don’t forget to delete the CloudFormation stack to avoid any unexpected cost. You need to empty the S3 buckets manually beforehand.

Conclusion

In this post, you learned how to customize your Amazon Translate translation jobs based on standard XLIFF fuzzy matching quality metrics. With this solution, you can greatly reduce the manual labor involved in reviewing machine translated text while also optimizing your usage of Amazon Translate. You can also extend the solution with data ingestion automation and workflow orchestration capabilities, as described in Speed Up Translation Jobs with a Fully Automated Translation System Assistant.

About the Authors

Narcisse Zekpa is a Solutions Architect based in Boston. He helps customers in the Northeast U.S. accelerate their adoption of the AWS Cloud, by providing architectural guidelines, design innovative, and scalable solutions. When Narcisse is not building, he enjoys spending time with his family, traveling, cooking, and playing basketball.

Dimitri Restaino is a Solutions Architect at AWS, based out of Brooklyn, New York. He works primarily with Healthcare and Financial Services companies in the North East, helping to design innovative and creative solutions to best serve their customers. Coming from a software development background, he is excited by the new possibilities that serverless technology can bring to the world. Outside of work, he loves to hike and explore the NYC food scene.



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