  Answering questions asked from instructional corpora such as E-manuals,
recipe books, etc., has been far less studied than open-domain factoid
context-based question answering. This can be primarily attributed to the
absence of standard benchmark datasets. In this paper we meticulously create a
large amount of data connected with E-manuals and develop suitable algorithm to
exploit it. We collect E-Manual Corpus, a huge corpus of 307,957 E-manuals and
pretrain RoBERTa on this large corpus. We create various benchmark QA datasets
which include question answer pairs curated by experts based upon two
E-manuals, real user questions from Community Question Answering Forum
pertaining to E-manuals etc. We introduce EMQAP (E-Manual Question Answering
Pipeline) that answers questions pertaining to electronics devices. Built upon
the pretrained RoBERTa, it harbors a supervised multi-task learning framework
which efficiently performs the dual tasks of identifying the section in the
E-manual where the answer can be found and the exact answer span within that
section. For E-Manual annotated question-answer pairs, we show an improvement
of about 40% in ROUGE-L F1 scores over the most competitive baseline. We
perform a detailed ablation study and establish the versatility of EMQAP across
different circumstances. The code and datasets are shared at
https://github.com/abhi1nandy2/EMNLP-2021-Findings, and the corresponding
project website is https://sites.google.com/view/emanualqa/home.
