  With the development of deep learning techniques and large scale datasets,
the question answering (QA) systems have been quickly improved, providing more
accurate and satisfying answers. However, current QA systems either focus on
the sentence-level answer, i.e., answer selection, or phrase-level answer,
i.e., machine reading comprehension. How to produce compositional answers has
not been throughout investigated. In compositional question answering, the
systems should assemble several supporting evidence from the document to
generate the final answer, which is more difficult than sentence-level or
phrase-level QA. In this paper, we present a large-scale compositional question
answering dataset containing more than 120k human-labeled questions. The answer
in this dataset is composed of discontiguous sentences in the corresponding
document. To tackle the ComQA problem, we proposed a hierarchical graph neural
networks, which represents the document from the low-level word to the
high-level sentence. We also devise a question selection and node selection
task for pre-training. Our proposed model achieves a significant improvement
over previous machine reading comprehension methods and pre-training methods.
Codes and dataset can be found at \url{https://github.com/benywon/ComQA}.
