Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework

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.