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Description
Develop applications utilizing conversational language understanding, an advanced AI capability that interprets user intentions and extracts crucial details from informal dialogue. Design customizable intent classification and entity extraction models tailored to your specific terminology across 96 different languages, allowing for multilingual functionality without the need for retraining after initial training in one language. Swiftly generate intents and entities while tagging your own utterances, and incorporate prebuilt components from an extensive range of standard types. Assess your models using integrated quantitative metrics such as precision and recall to ensure optimal performance. A user-friendly dashboard simplifies the management of model deployments within the accessible language studio. Effortlessly integrate with various other features in Azure AI Language, alongside Azure Bot Service, to create a comprehensive conversational experience. This conversational language understanding represents the evolution of Language Understanding (LUIS) and enhances the way users interact with technology. As the demand for intuitive communication increases, leveraging this technology can significantly improve user engagement and satisfaction.
Description
Bitext specializes in creating multilingual hybrid synthetic training datasets tailored for intent recognition and the fine-tuning of language models. These datasets combine extensive synthetic text generation with careful expert curation and detailed linguistic annotation, which encompasses various aspects like lexical, syntactic, semantic, register, and stylistic diversity, all aimed at improving the understanding, precision, and adaptability of conversational models. For instance, their open-source customer support dataset includes approximately 27,000 question-and-answer pairs, totaling around 3.57 million tokens, 27 distinct intents across 10 categories, 30 types of entities, and 12 tags for language generation, all meticulously anonymized to meet privacy, bias reduction, and anti-hallucination criteria. Additionally, Bitext provides industry-specific datasets, such as those for travel and banking, and caters to over 20 sectors in various languages while achieving an impressive accuracy rate exceeding 95%. Their innovative hybrid methodology guarantees that the training data is not only scalable and multilingual but also compliant with privacy standards, effectively reduces bias, and is well-prepared for the enhancement and deployment of language models. This comprehensive approach positions Bitext as a leader in delivering high-quality training resources for advanced conversational AI systems.
API Access
Has API
No
API Access
Has API
No
Integrations
Azure AI Bot Service
Yes
Azure AI Services
Yes
Hugging Face
No
LUIS
Yes
Microsoft Azure
Yes
Microsoft Bot Framework
Yes
Integrations
Azure AI Bot Service
No
Azure AI Services
No
Hugging Face
Yes
LUIS
No
Microsoft Azure
No
Microsoft Bot Framework
No
Pricing Details
$2 per month
Free Trial
Yes
Free Version
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
Yes
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
Yes
Vendor Details
Company Name
Microsoft
Founded
1975
Country
United States
Website
azure.microsoft.com/en-us/products/ai-services/conversational-language-understanding/
Vendor Details
Company Name
Bitext
Founded
2008
Country
United States
Website
www.bitext.com/training-datasets/
Product Features
Natural Language Processing
Co-Reference Resolution
No
In-Database Text Analytics
No
Named Entity Recognition
No
Natural Language Generation (NLG)
No
Open Source Integrations
No
Parsing
No
Part-of-Speech Tagging
No
Sentence Segmentation
No
Stemming/Lemmatization
No
Tokenization
No