Synthetic Training data is the data that is used to train an NLU engine. An NLU engine allows chatbots to understand the intent of user queries. The training data is enriched by data labeling or data annotation, with information about entities, slots…
This training process provides the bot with the ability to hold a meaningful conversation with real people.
After the training process, the bot is evaluated to measure the accuracy of the NLU engine. Evaluation identifies errors in the bot behavior and these errors are then fixed by improving training data. This cycle is repeated.
Industrialize training data production for any voice-controlled device, chatbot or IVR using artificial training data.
Machine Learning is one of the most common use cases for Synthetic Data today, mainly in images or videos.
Bitext Synthetic Training Data can resolve all of those 3 problems listed above and We offer text training data in any language you need. Quickly scale or increase the amount of data in a fast and flexible way.
We help you understand your customers either
Bitext has solutions for your current bot and for your new bot.
Next step, after training , is to evaluate data. We explain better this proccess with Unstructured Synthetic Text topic. Take a look!
Our earlier German MIRACL benchmark showed that linguistic analysis can substantially improve lexical search in…
Vector search, also known as semantic search, has transformed enterprise search in the past few…
In a search benchmark for German, Bitext Linguistic Analysis SDK returned more relevant results and…
Search systems have relied on stemming for decades. The reason is simple: stemming is fast,…
Most teams working with Elasticsearch, OpenSearch or RAG pipelines focus on ranking, embeddings or model…
Some RAG issues have a simpler fix than people think: better text normalization. One common…