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- Introduction
- Setting up your account
- Balance
- Clusters
- Concept drift
- Coverage
- Datasets
- General fields
- Labels (predictions, confidence levels, label hierarchy, and label sentiment)
- Models
- Streams
- Model Rating
- Projects
- Precision
- Recall
- Annotated and unannotated messages
- Extraction Fields
- Sources
- Taxonomies
- Training
- True and false positive and negative predictions
- Validation
- Messages
- Access control and administration
- Manage sources and datasets
- Understanding the data structure and permissions
- Creating or deleting a data source in the GUI
- Preparing data for .CSV upload
- Uploading a CSV file into a source
- Creating a dataset
- Multilingual sources and datasets
- Enabling sentiment on a dataset
- Amending dataset settings
- Deleting a message
- Deleting a dataset
- Exporting a dataset
- Using Exchange integrations
- Model training and maintenance
- Understanding labels, general fields, and metadata
- Label hierarchy and best practices
- Comparing analytics and automation use cases
- Turning your objectives into labels
- Overview of the model training process
- Generative Annotation
- Dastaset status
- Model training and annotating best practice
- Training with label sentiment analysis enabled
- Understanding data requirements
- Train
- Introduction to Refine
- Precision and recall explained
- Precision and Recall
- How validation works
- Understanding and improving model performance
- Reasons for label low average precision
- Training using Check label and Missed label
- Training using Teach label (Refine)
- Training using Search (Refine)
- Understanding and increasing coverage
- Improving Balance and using Rebalance
- When to stop training your model
- Using general fields
- Generative extraction
- Using analytics and monitoring
- Automations and Communications Mining™
- Developer
- Uploading data
- Downloading data
- Exchange Integration with Azure service user
- Exchange Integration with Azure Application Authentication
- Exchange Integration with Azure Application Authentication and Graph
- Fetching data for Tableau with Python
- Elasticsearch integration
- General field extraction
- Self-hosted Exchange integration
- UiPath® Automation Framework
- UiPath® official activities
- How machines learn to understand words: a guide to embeddings in NLP
- Prompt-based learning with Transformers
- Efficient Transformers II: knowledge distillation & fine-tuning
- Efficient Transformers I: attention mechanisms
- Deep hierarchical unsupervised intent modelling: getting value without training data
- Fixing annotating bias with Communications Mining™
- Active learning: better ML models in less time
- It's all in the numbers - assessing model performance with metrics
- Why model validation is important
- Comparing Communications Mining™ and Google AutoML for conversational data intelligence
- Licensing
- FAQs and more
Communications Mining user guide
Last updated Mar 6, 2026
Multilingual sources and datasets
Communications Mining™ supports multilingual sources and datasets. This means that the models can understand sources that contain multiple different supported languages, without actually having to translate them.
The languages available within multilingual sources and datasets are:
- English
- Dutch
- French
- German
- Italian
- Japanese
- Portuguese
- Spanish
If you work and do business in several languages that the platform supports, you can train on messages in those languages, rather than translating everything into a single language.
Key considerations
- If a dataset is multilingual, you cannot view translations of any messages, as provided for translated datasets. As a result, you will need to understand all of the languages in the dataset to effectively train their model.
- Understanding multiple languages is a more complex machine-learning problem than understanding a single language. As a result, these datasets may potentially experience a slight drop in performance compared to datasets in a single language.
- If the dataset contains other languages than the supported ones, applying labels used for supported languages may cause confusion. Instead, annotate these instances with language-specific labels.
Note:
The platform cannot process or understand the content of unsupported languages.
Creating multilingual sources and datasets
When creating a data source or a dataset, the platform selects by default the English language for both of them.
To change the language while creating your data source or dataset, proceed as follows:
- Navigate to the Set the language, and enable translation for your source step.
- In the Language dropdown menu, select Multilingual.
Note:
- You can no longer change the language once the data source or dataset is created.
- Multilingual datasets can contain sources of any language family that the platform supports.
- To learn how to create data sources and datasets, check Creating a data source and Creating a dataset.
Supported languages in Preview
Note:
Register on the Insider Portal to provide feedback or raise issues.
We currently support a wide range of additional languages in Preview mode, as shown in the following list. This means that our team refines them based on your usage.
- Afrikaans
- Albanian
- Amharic
- Arabic
- Armenian
- Assamese
- Azerbaijani
- Basque
- Belarusian
- Bengali
- Bengali (Romanized)
- Bosnian
- Breton
- Bulgarian
- Burmese
- Burmese
- Catalan
- Chinese (Simplified)
- Chinese (Traditional)
- Croatian
- Czech
- Danish
- Esperanto
- Estonian
- Filipino
- Finnish
- Galician
- Georgian
- Greek
- Gujarati
- Hausa
- Hebrew
- Hindi
- Hindi (Romanized)
- Hungarian
- Icelandic
- Indonesian
- Irish
- Javanese
- Kannada
- Kazakh
- Khmer
- Korean
- Kurdish (Kurmanji)
- Kyrgyz
- Lao
- Latin
- Latvian
- Lithuanian
- Macedonian
- Malagasy
- Malay
- Malayalam
- Marathi
- Mongolian
- Nepali
- Norwegian
- Oriya
- Oromo
- Pashto
- Persian
- Polish
- Punjabi
- Romanian
- Russian
- Sanskrit
- Scottish Gaelic
- Serbian
- Sindhi
- Sinhala
- Slovak
- Slovenian
- Somali
- Sundanese
- Swahili
- Swedish
- Swiss German
- Tamil
- Tamil (Romanized)
- Telugu
- Telugu (Romanized)
- Thai
- Turkish
- Ukrainian
- Urdu
- Urdu (Romanized)
- Uyghur
- Uzbek
- Vietnamese
- Welsh
- Western Frisian
- Xhosa
- Yiddish