Michael Heilman Noah A. Smith
The Learning to Ask paper uses this as a baseline. This paper came out in 2010 before the current wave of neural approaches- embeddings, rnns etc.
Basic idea is to successively transform using rules, a declarative source sentence into a simpler sentence, and then into a question phrase. Does not even attempt complex queries requiring multiple sentences. As there are many rules, multiple questions can be generated from the same sentence. A logistic regression model is trained to judge the quality of these questions and eliminate bad ones. Essentially a feature based classifier. The authors refer to this as the overgenerate and rank approach.
Things I learned
- A rule based system is hard. This looks difficult, time consuming work. And difficult to scale and test as with most rule based systems. But I suppose it’s good as a baseline.
- The transformation implicitly determines the type of question asked ie. the quality of the question asked relies on the strength of transformation
- Transformations are done using a language called Tregex, which works on phrase structure trees produced by the Stanford Parser.
- Every sentence is guaranteed to have multiple types of questions due to the rule based sentence-question transformation. Not so in the Learning To Ask paradigm.
- Question answer datasets were far smaller then. Neural approaches are possibly only feasible now.
- Really need to understand what exactly a parser is. What are POS tags, category labels?
- What is a dependency tree? Is it the same as above?
- How do neural learning to rank models work? Is it just a name given to scoring a bunch of items using logistic function and then ranking them?