The context would be known upfront, and the training examples would be optionally annotated with a context. This would provide a conditioning option to perform NLU upon a contextualized set of intents.
For bigger domains we might have intent ambiguity when users say something in response of a bot request, because the real meaning of a sentence may vary according to context. If we capture the context on the first interactions we might improve the intent classification by context-conditioning the next interactions. The human brain is excellent on reasoning because it does a lot of context filtering.
My use case is a bit special as I’m integrating Rasa NLU on an existing chatbot framework, and the only way I can integrate the NLU modules is by tapping into the intent classification phase. This framework provides an high level domain language for creating “conversations” around known objects the application provides. The conversation revolves around the objects and the information those objects store in a database. Since this “objects” are known and well described, the training examples are automatically generated around what is defined the “domain”, which includes all the objects.
An example of a typical conversation anotaded with context information:
U: "Hey you! I want a list of scientific papers about on the Biology domain".
B: "Sure thing! I have around 379 papers about that, want me to send you in an email?"
U: "No thanks, but can you find me those with the keywords abc and xyz?"
B: "Yep, there are 9, let me put here the titles. etc etc"
U: "Cool. Show me also the abstracts please?"
B: "Here they are! Etc etc"
U: "Ok now, can you list all conferences for the Biology domain?"
B: There are plenty! Want me to send you in an email?
U: "Nah, but if you could find me those with previous keywords would be sweet!"
B: Sure thing boss!
As you can see there is a lot of context moving around. Some of the context the framework will store and use in if need in later user requests. The keywords abc and xyz are an example of that.
There is also a lot going on on context navigation here both paper or conference “objects” have quasi similar intents for "find me all with keyworks ". Example phrases are generated on a top level context, where the name is needed on the user request, so the chatbot will know what object the query corresponds. Then “object” context bound examples are generated for contextualized queries:
U: Can you read me the abstracts?
U: And what about the authors?
I believe NLU inference with context conditioning can be powerful.
Now, for easy solving this for me, would it work if I prefix the training sets with the context information and use that when inferring?
// Examples for paper
PAPER PAPER PAPER find me all papers
PAPER PAPER PAPER find me all scientific papers
PAPER PAPER PAPER who are the authors
// Example of conferences
CONFERENCE CONFERENCE CONFERENCE find me all conferences
Then when U: asks “who are the authors?” the framework knowing the context of paper could prefix the phrase with CONFERENCE CONFERENCE CONFERENCE before sending in the NLU API.
You think this would work?