  The task of Reading Comprehension with Multiple Choice Questions, requires a
human (or machine) to read a given passage, question pair and select one of the
n given options. The current state of the art model for this task first
computes a question-aware representation for the passage and then selects the
option which has the maximum similarity with this representation. However, when
humans perform this task they do not just focus on option selection but use a
combination of elimination and selection. Specifically, a human would first try
to eliminate the most irrelevant option and then read the passage again in the
light of this new information (and perhaps ignore portions corresponding to the
eliminated option). This process could be repeated multiple times till the
reader is finally ready to select the correct option. We propose ElimiNet, a
neural network-based model which tries to mimic this process. Specifically, it
has gates which decide whether an option can be eliminated given the passage,
question pair and if so it tries to make the passage representation orthogonal
to this eliminated option (akin to ignoring portions of the passage
corresponding to the eliminated option). The model makes multiple rounds of
partial elimination to refine the passage representation and finally uses a
selection module to pick the best option. We evaluate our model on the recently
released large scale RACE dataset and show that it outperforms the current
state of the art model on 7 out of the $13$ question types in this dataset.
Further, we show that taking an ensemble of our elimination-selection based
method with a selection based method gives us an improvement of 3.1% over the
best-reported performance on this dataset.
