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 query-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 document 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 {document, question} pair and if so it tries to make the document representation orthogonal to this eliminatedd option (akin to ignoring portions of the document corresponding to the eliminated option). The model makes multiple rounds of partial elimination to refine the document 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 7% (relative) over the best reported performance on this dataset.
In this paper, a model is built for reading comprehension with multiple choices. The model consists of three modules: encoder, interaction module and elimination module. The major contributions are two folds: firstly, proposing the interesting option elimination problem for multi-step reading comprehension;  and secondly, proposing the elimination module where a eliminate gate is used to select different orthogonal factors from the document representations. Intuitively, one answer option can be viewed as eliminated if the document representation vector has its factor along the option vector ignored.
The elimination module is interesting, but the usefulness of “elimination” is not well justified for two reasons. First, the improvement of the proposed model over the previous state of the art is limited. Second, the model is built upon GAR until the elimination module, then according to Table 1 it seems to indicate that the elimination module does not help significantly (0.4% improvement). 
In order to show the usefulness of the elimination module, the model should be exactly built on the GAR with an additional elimination module (i.e. after removing the elimination module, the performance should be similar to GAR but not something significantly worse with a 42.58% accuracy). Then we can explicitly compare the performance between GAR and the GAR w/ elimination module to tell how much the new module helps.
Other issues:
1) Is there any difference to directly use $x$ and $h^z$ instead of $x^e$ and $x^r$ to compute $\tilde{x}_i$? Even though the authors find the orthogonal vectors, they’re gated summed together very soon. It would be better to show how much “elimination” and “subtraction” effect the final performance, besides the effect of subtraction gate.
2) A figure showing the model architecture and the corresponding QA process will better help the readers understand the proposed model.
3) $c_i$ in page 5 is not defined. What’s the performance of only using $s_i$ for answer selection or replacing $x^L$ with $s_i$ in score function?
4) It would be better to have the experiments trained with different $n$ to show how multi-hop effects the final performance, besides the case study in Figure 3.
Minor issues:
1) In Eqn. (4), it would be better to use a vector as the input of softmax.
2) It would be easier for discussion if the authors could assign numbers to every equation.