Summary: Your Data Speaks: Story, Questions and Answers

In this chapter, we found that question answering isn’t as easy as it seems. Implementing a transformer model only takes a few minutes. However, getting it to work can take a few hours or several months!

We first asked the default transformer in the Hugging Face pipeline to answer some simple questions. DistilBERT, the default transformer, answered the simple questions quite well. However, we chose easy questions. In real life, users ask all kinds of questions. The transformer can get confused and produce erroneous output.

We then realized we could continue to ask random questions and get random answers, or we could begin to design the blueprint of a question generator, which is a more productive solution.

We started by using NER to find useful content. We designed a function that could automatically create questions based on NER output. The quality was promising but required more work.

We tried an ELECTRA model that did not produce the results we expected. We stopped for a few minutes to decide if we would spend costly resources to train transformer models or design a question generator.

We added SRL to the blueprint of the question generator and tested the questions it could produce. We also added NER to the analysis and generated several meaningful questions. We also discovered other ways of addressing question-answering with RoBERTa.