**How would you describe** [[ ]{}]{}? & Comets are [[ ]{}]{}. & **How would you describe** [[ ]{}]{} ?\

In each of the templates, we convert *What* to *Who/Whom*, *When* or *Where* depending on the named entity of the potential answer and *do* to *does* or *did* according to the tense and number of the subject to ensure subject-verb agreement. The pseudo code is described in Algorithm \[alg:the\_alg\] of the Appendix.

SRL Heuristics
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While dependency representations are perhaps the most popular syntactic method for automatically extracting relationships between words, they lack sufficient semantic detail. Being able to answer *“Who* did *what* to *whom* and *how, why, when* and *where”* has been a central focus in understanding language. In recent decades, shallow semantic parsing has been a prominent choice in understanding these relationships and has been extensively used in question generation [@mazidi2016infusing; @flor2018semantic].

PropBank-style frames provide semantically motivated roles that arguments around a verb play. Moreover, highly accurate semantic role labeling models are being developed owing to corpora like PropBank and FrameNet. We take advantage of the SRL model of @gardner2018allennlp for extracting the roles of each verb in the sentence.

$\{SRL_1 \ldots SRL_s \} \gets SRL(w_0 \ldots w_n)$ *loop 