Title: Iterative Hierarchical Attention for Answering Complex Questions over Long Documents. Abstract: We propose a new model, DocHopper, that iteratively attends to different parts of long, hierarchically structured documents to answer complex questions. Similar to multi-hop question-answering (QA) systems, at each step, DocHopper uses a query q to attend to information from a document, combines this “retrieved” information with q to produce the next query. However, in contrast to most previous multi-hop QA systems,  DocHopper is able to “retrieve” either short passages or long sections of the document, thus emulating a multi-step process of “navigating” through a long document to answer a question. To enable this novel behavior, DocHopper does not combine document information with q by concatenating text to the text of q, but by combining a compact neural representation of q with a compact neural representation of a hierarchical part of the document -- potentially a large part.  We experiment with DocHopper on four different QA tasks that require reading long and complex documents to answer multi-hop questions, and show that DocHopper outperforms all baseline models and achieves state-of-the-art results on all datasets. Additionally, DocHopper is efficient at inference time, being 3 - 10 times faster than the baselines.