The dataset and the source code for the system are available at <https://github.com/xinyadu/HarvestingQA>.

Related Work {#sec:related}
============

Question Generation
-------------------

Since the work by , question generation (QG) has attracted interest from both the NLP and NLG communities. Most early work in QG employed rule-based approaches to transform input text into questions, usually requiring the application of a sequence of well-designed general rules or templates [@mitkov2003computer; @labutov2015deep].  introduced an overgenerate-and-rank approach: their system generates a set of questions and then ranks them to select the top candidates. Apart from generating questions from raw text, there has also been research on question generation from symbolic representations [@yao2012semantics; @olney2012question]. With the recent development of deep representation learning and large QA datasets, there has been research on recurrent neural network based approaches for question generation.  used the encoder-decoder framework to generate QA pairs from knowledge base triples;   generated questions from a knowledge graph;   studied how to generate questions from sentences using an attention-based sequence-to-sequence model and investigated the effect of exploiting sentence- vs. paragraph-level information.  proposed a hierarchical neural sentence-level sequence tagging model for identifying question-worthy sentences in a text passage. Finally, investigated how to use question generation to help improve question answering 