  Multimedia or spoken content presents more attractive information than plain
text content, but it's more difficult to display on a screen and be selected by
a user. As a result, accessing large collections of the former is much more
difficult and time-consuming than the latter for humans. It's highly attractive
to develop a machine which can automatically understand spoken content and
summarize the key information for humans to browse over. In this endeavor, we
propose a new task of machine comprehension of spoken content. We define the
initial goal as the listening comprehension test of TOEFL, a challenging
academic English examination for English learners whose native language is not
English. We further propose an Attention-based Multi-hop Recurrent Neural
Network (AMRNN) architecture for this task, achieving encouraging results in
the initial tests. Initial results also have shown that word-level attention is
probably more robust than sentence-level attention for this task with ASR
errors.
