Train Your Virtual Assistant
with industry-leading ML+2 NLP engine
Virtual assistants built on the Kore.ai Platform use machine learning plus multiple NLP strategies—fundamental meaning and knowledge graph—to improve the intent recognition rate. Create intelligent Assistants that are better prepared to converse with your users and convert them to happy customers.
Training with Machine Learning
Machine Learning is ideal when you already have sample sentences for each intent. It allows you to train your Intents/Tasks with sample data and learns using the Deep Neural Network models and accurately predicts for more varied inputs from the user. Ex: "I want to pay," can be a sample utterance for the intent as Pay bill.Named Entity Recognition model allows you to mark entities from the user utterances and train the assistant to identify them accurately. System entities like Date, Number, Currency, Country etc are identified out of the box. NER training helps even in predicting the Custom or Domain-driven entities like Account Types, Card Type etc.Kore.ai allows a VA developer to choose the ML model relevant for their Data by providing the choice of cutting edge DNN based models like LSTM, CNN, KAEN. We can integrate any 3rd party ML engine too if required. Also, configure the agent to auto-learn from the successfully executed tasks.
Train with Knowledge Graph
The Knowledge Graph engine is ideal if you are working on a large set of intents in the form of questions. Import large volumes of information quickly, group and annotate the key terms, add synonyms, provide alternate queries. Optionally we can build a hierarchy.
Alternatively, download the Ontology generator from Github to automatically generate the knowledge graph. Knowledge Extraction engine allows one to extract information from unstructured documents and query on them.
Train with Fundamental Meaning
Use the Fundamental Meaning engine to train idiomatic or command-like sentences. The engine uses a semantic approach to understanding grammar and nuances, empowering VAs to associate synonyms with task names. This mapping improves intent recognition rates and, when combined with patterns, can be used to train VAs to recognize idiomatic expressions.
Decipher Twisted Utterances
by configuring Traits
Traits are specific entities, attributes or details that the users express in their conversations. It is common for users to provide background information in natural conversations while describing a scenario. Traits as a feature use this background information to identify the intent and accordingly drive the conversational flow.
For instance, if “I am looking for a low-cost option to London” is the user’s utterance. The assistant has to identify the intent as flight booking.
Proactive NLU Validation
for superior accuracy
Virtual assistants perform best when you train them with accurate and adequate data. To help you provide superior training, the XO Platform provides proactive validations. It guides you to improve the VAs’ performance by constantly validating the NLU model and alerts you with errors and warning notifications.
The update focuses on validations related to the intent training with the ML & FM Engines for untrained intents, inadequate training, utterance does not qualify any intent, utterance predicts an incorrect intent, utterance predicts the expected intent with low confidence, incorrect intent patterns, short training utterances, incorrect entity annotations and more.