I am building a neural network to do intent detection and slot filling. The results I am getting are somewhat poor. Hence, I am looking for an approach to improve my results.

My idea is to use multi-task learning (like in Collobert and Westonäs paper) to take advantage of an additional's task knowledge for improving the results.

So far, I have been thinking about using a question-answering approach (sequence-to-sequence) that would share the intermediate representation with the rest of my architecture.

Do you think that question-answering would be a good task in this case?

The rationale behind this choice is that question-answering would need to learn a representation of the input sentence's intent in order to be able to generate an answer.

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.