Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework (EMNLP Findings 2021)

Abstract

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.

Paper - https://arxiv.org/abs/2109.05897

Data, model, slides, blog, poster, code and video

Pre-training corpus (it is allocated in the form of in 38 zipped files. If you unzip each file, you will get about 8300 text files for each zip)

A RoBERTa BASE Model pre-trained on the corpus can be found here, and a BERT BASE UNCASED Model pre-trained on the same here.

Presentation slides

Blog Post

Poster

Github Repo for the same can be found here.

Explanation video can be found here.

Cite as -

@inproceedings{nandy-etal-2021-question-answering,

title = "Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based {QA} Framework",

author = "Nandy, Abhilash and

Sharma, Soumya and

Maddhashiya, Shubham and

Sachdeva, Kapil and

Goyal, Pawan and

Ganguly, NIloy",

booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",

month = nov,

year = "2021",

address = "Punta Cana, Dominican Republic",

publisher = "Association for Computational Linguistics",

url = "https://aclanthology.org/2021.findings-emnlp.392",

doi = "10.18653/v1/2021.findings-emnlp.392",

pages = "4600--4609",

abstract = "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 a 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 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.",

}