vs. the gold standard SQuAD answers. Since answer boundaries are sometimes ambiguous, we compute [*Binary Overlap*]{} and [*Proportional Overlap*]{} metrics in addition to [*Exact Match*]{}. Binary Overlap counts every predicted answer that overlaps with a gold answer span as correct, and Proportional Overlap give partial credit proportional to the amount of overlap [@johansson2010syntactic; @irsoy2014opinion].

\[tab:qg\]

Baselines and Ablation Tests {#sec:base}
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For question generation, we compare to the state-of-the-art baselines and conduct ablation tests as follows: [****]{}’s model is an attention-based RNN sequence-to-sequence neural network (without using the answer location information feature). [**Seq2seq + copy**]{}~**w/\ answer**~ is the attention-based sequence-to-sequence model augmented with a copy mechanism, with answer features concatenated with the word embeddings during encoding. [**Seq2seq + copy**]{}~**w/\ full\ context\ +\ answer**~ is the same model as the previous one, but we allow access to the full context (i.e., all the preceding sentences and the input sentence itself). We denote it as [**ContextNQG**]{} henceforth for simplicity. [**CorefNQG**]{} is the coreference-based model proposed in this paper. [**CorefNQG–gating**]{} is an ablation test, the gating network is removed and the coreference position embedding is not refined. [**CorefNQG–mention-pair score**]{} is also an ablation test where all mention-pair $score_i$ are set to zero.

For 