in the top 50%.

Back-Translation
----------------

Back-translation has been used quite often in grammatical error correction [@xie2018noising] and is well known to translate noisy and ungrammatical sentences to their cleaner high probability counterparts. We exploit this observation to clean questions with noisy and inconsistent PropBank descriptions like “wanter” (Figure \[fig:PB3\]). We use two state-of-the-art (SOTA) pre-trained transformer models `transformer.wmt19.en-de` and `transformer.wmt19.de-en` from @ott2019fairseq trained on the English-German and German-English translation tasks of WMT 2019.

Figure \[fig:synqg\_all\] in the Appendix shows the output of all the five sets of templates applied together over one sentence (along-with implicature).

Evaluation and Results
======================

[| M[8.7cm]{} | L[1.4cm]{} | C[1.4cm]{} | R[1.4cm]{} | R[1.4cm]{} |]{} **Architecture** & BLEU-1 & BLEU-2 & BLEU-3 & BLEU-4\
PCFG-Trans [@heilman2010good] & 28.77 & 17.81 & 12.64 & 9.47\
SeqCopyNet [@zhou2018sequential] & & & & 13.02\
NQG++ [@zhou2017neural] & 42.36 & 26.33 & 18.46 & 13.51\
MPQG [@song2017unified] & & & & 13.91\
Answer-focused Position-aware model [@sun2018answer] & 43.02 & 28.14 & 20.51 & 15.64\
To the Point Context [@li2019improving] & 44.40 & 29.48 & 21.54 & 16.37\
s2sa-at-mp-gsa [@zhao2018paragraph] & 44.51 & 29.07 & 21.06 & 15.82\
ASs2s [@kim2019improving] & & & & 16.17\
CGC-QG [@liu2019learning] & 46.58 