multilingual synthetic training data

Conversational AI
up and running without effort

Conversational AI
up and running without effort

Industrialize training data production for any voice-controlled device, chatbot or IVR using artificial training data.

Accelerate the deployment with our automated solution: from our pre-built vertical templates (bootstrapping) covering the most common intents for 20 verticals in 9 languages, to custom datasets for customer specific requests or data augmentation.

Multilingual Training datasets for intent detection

Our Conversational AI is based on industry-agnostic NLP engine that has around 95% of intention recognition accuracy. It understands free speech, not just predefined phrases.

Either if you do not have any existing training data or need to increase your accuracy or expand to other languages with consistency, we can help you.

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Reduce Time to Market

Deliver effortless customer support by avoiding manual training data.

Customized for different domains & languages

Boostrapping with our templates: 20 verticals in 9 languages (English, Spanish, French, Italian, German, Dutch, Danish, Swedish and Portuguese).

Easy Integration

Customized data to integrate on any platform (Dialogflow, LUIS, Lex…) with up to 40% accuracy improvement.

Data Augmentation

If you already have your own training data but would need to expand the scope (intents, variants…) or increase the accuracy.

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Scalability & Modularity

Consistency between intents, verticals and languages. Enables fast error correction and speed of retraining.

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Full Data Protection

Avoid data privacy issues. We do not use personal data: no need consent or regulatory obligations. 

Sentiment Analysis

We help you routing or understanding the customer journey, get customer insights and quality assurance services.

Representative Vendor

Bitext is mentioned by Gartner in 4 Reports as representative vendor for Synthetic Data in 2019.

A Benchmark based on Dialogflow shows increased accuracy up to 40%.

See how automatic training improves manual training.

Get the full dataset used to generate the benchmark results. Check out how easy is to integrate the training data into Dialogflow and get 40% increased accuracy.

We begin by collecting large volumes of text from domain-specific public data sources such as FAQs, knowledge bases and technical documentation.

We then apply our Deep Parsing technology to automatically extract the most frequent actions and objects that appear in those texts. This results in a knowledge graph that captures the semantic structure of the vertical, which is then curated by computational linguists to identify synonyms and to ensure consistency and completeness.

Actions are grouped into categories and intents, and the intent structure is then validated against FAQs and with domain experts.

Finally, the linguistic structure of each intent is defined, together with the applicable frame types which allow our Natural Language Generation (NLG) technology to generate utterances which are predictable and consistent semantic variations of each intent request.

Are you still training manually?

Building effective conversational agents requires large amounts of training data.

Producing this data manually is an expensive, time-consuming and error-prone process which does not scale.

Platform providers usually do not have the infrastructure required to tackle the wide range of verticals, languages and locales that their large clients need to handle, while clients rarely have the expertise necessary to collect and annotate their data to avoid both language ambiguity and intent overlap when models are trained.

 

methodology

We begin by collecting large volumes of text from domain-specific public data sources such as FAQs, knowledge bases and technical documentation.

We then apply our Deep Parsing technology to automatically extract the most frequent actions and objects that appear in those texts. This results in a knowledge graph that captures the semantic structure of the vertical, which is then curated by computational linguists to identify synonyms and to ensure consistency and completeness.

Actions are grouped into categories and intents, and the intent structure is then validated against FAQs and with domain experts.

Finally, the linguistic structure of each intent is defined, together with the applicable frame types which allow our Natural Language Generation (NLG) technology to generate utterances which are predictable and consistent semantic variations of each intent request.

We employ a scalable and data-driven linguist-in-the-loop methodology.

This approach provides a measurable improvement to NLU performance: benchmarks comparing a manual baseline with our synthetic data show >30% increase in intent detection and slot filling accuracy across multiple platforms.

Our methodology and tools allow us to easily customize and adapt datasets to changing needs, including new intents, corporate terminology, language registers, new regions, markets and languages. With each change, the data is automatically regenerated, allowing for continuous improvement in a scalable fashion.

Why We are different

Easy Implementation

Based on Data

Typically up & running

in days not months

Dedicated Client Success Managers

highly skilled in NLP options

Platform agnostic

Ready for Dialogflow, LUIS, Lex, Watson, Rasa and many more

Recycle and own your data

for different platforms and projects

API On Premise

Flexible delivery options

Multilingual synthetic data

Machine Learning is one of the most common use cases for Synthetic Data today mainly in images or videos. We offer text training data in any language you need. Quickly scale or increase the amount of data in a fast and flexible way.

If you need to build from scratch

if you have existing training data

Generation of Multilingual Training Data:

  • We offer different options according to your needs. From our pre-built vertical templates (bootstrapping) covering the most common intents for each vertical, to custom datasets for customer specific requests. 

  • We can add advanced module datasets covering regional variants, politeness, expanded abreviations, offensive or small talk…

if you have existing training data

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Augmented Synthetic Training Data

  • If you want to increase the accuracy or expand the scope with more intents or utterances.

  • We can automate the process and generate the training data you need in any language.

verticals available

Retail - Ecommerce

Travel

Retail Banking

Utilities

Media Streaming

Telecom

Insurance

Field Service

Hospitality

Manufacturing

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Automotive

Healthcare

Mortages & Loans

Wealth Management

Real State/ Construction

Education

Restaurant & Bar chains

Moving & Storage

Events & Ticketing

Legal Services

Got questions?

Other Services

ML Engines - Core NLP Services

Build the most accurate multilingual NLP infrastructure for your AI engine.

Include: Lemmatization, POS tagging, language identification and much more…

CX Analytics

Get insights into what your international customers say about your company with 90% accuracy.  Include: topic-based Sentiment analysis, categorization and anonymization.

Case studies

NLP Solutions for AI Engines

Lemmatization 

NLP Tool for your Machine Learning/ DL Engine

Customer Experience (CX)

Automotive Industry Case Study

Multilingual NLP engine from scratch

Virtual Assistants

Techcruch Case Study

Multilingual Data for training bots to increase accuracy 

Our customers

Working with 3 of the Top 5 largest companies in NASDAQ

Working with 3 of the Top 5 largest companies in NASDAQ

empower your contact center with our api

You can find in our NLP API Platform a wide variety of our leading multilingual NLP tools and solutions that will help you create the best customer experience. Here are some examples:

Sentiment Analysis

Identify the topics of conversation and evaluate, extract and quantify the emotional state or attitude of your customers towards those topics with a polarity score value.

Language Identification

Detects the language of the input text and returns a list of sentences with their respective language.

Our approach takes advantage of our wide range of linguistic resources, including our computational lexicons and morphological models.

Lemmatization

Extract the relevant multi-word noun, verb, adjective or adverbial phrases using morphological and syntactic analysis.

Anonymization

Data processing technique that removes or replace personally identifiable information with special tokens. The result is anonymized data that cannot be associated with any one individual. 

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Entity Extraction

Extract the relevant multi-word noun, verb, adjective or adverbial phrases using morphological and syntactic analysis.

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Categorization

Categorization service classifies your texts into groups according to your customized categories.

Bitext can improve the performance of almost any conversational engine and project”.

“End users frustrated with the performance or complexity of their chatbot developments will be interested in how Bitext can improve intent matching confidence and reduce development time”.

“Synthetic data can act as a democratizer for smaller players as they try to compete with data-laden tech heavyweights. Privacy restrictions are an additional major driver of this technology”.

Anthony Mullen- Gartner

2018 Cool Vendors in AI Core Technologies Report & Hype Cycle for Enterprise Information Management, 2019

Bitext is recognized in up to 20 Gartner Reports

We have received this recognition because of our relentless focus on product innovation: Synthetic Data and NLP Middleware are some of our cutting-edge technologies.

Benchmark-on-amazon-lex-bitext

Benchmark on Amazon Lex

Check out how we improved Amazon Lex accuracy by 50% using our training data

Microsoft-LUIS-bitext

Benchmark on Microsoft LUIS

Increase accuracy on the LUIS platform up to 40% using Bitext training data

Benchmark-dialogflow-bitext

Benchmark on Dialogflow

A benchmark based on Dialogflow shows accuracy increases of up to 40%

Bitext Word Embeddings

Benchmark on Entity Extraction

This report compares Bitext’s entity extraction software to 3 other engines (CRFSuite, Stanford and SENNA)

Benchmark-lemmatization-bitext

Benchmark on Lemmatization

A brief comparison of stemmers and lemmatizers

SAN FRANCISCO, USA

541 Jefferson Ave., Ste. 100

Redwood City

CA 94063

MADRID, SPAIN

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Parque Empresarial Las Rozas

28232 Las Rozas