NLU Internships at tact.ai

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Employer: tact.ai

Expires: 06/22/2020

Our goal is to build a robust NLU system to go from NL to database queries while being integrated with dialog (for example for disambiguation). These are some of the things we want to try:1 semantic parser: 1.1 Custom system based on a seq2seq model using a pointer network and user specific features generators 1.2 System based on manually specified grammar and lexicon 1.2.1 using frameworks/formalisms like: Sempre/CCG/AMR 1.2.2 how to learn the lexicon from data 1.2 a module to reconstruct the NL that corresponds to a specific query 1.3 a module to explain failures in understanding back to the user The queries are used in a dialog system. This is not a pure QA system. For example, if you ask to update a certain record for a certain person, and the system retrieves multiple possible persons, you need to ask the user to pick one before proceeding. How/where do you specify this logic with respect to the NLU module?2 define natural ways to define a dialog policy, e.g. a definition language in controlled english compiled down into a fully specified policy.These are just some of the ideas we would like to try.Questions for the candidate:Think about the above problems/suggestions and describe possible projects that would fit with your thesis goalsWhat are the languages you are most comfortable with?What’s the largest system you had to modifyWhat machine learning techniques/applications have you tried in the past?Some references about us:https://tact.ai/https://www.crunchbase.com/organization/tactile