Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework
Abhilash Nandy, Soumya Sharma, Shubham Maddhashiya, Kapil Sachdeva, Pawan Goyal, NIloy Ganguly
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.- Anthology ID:
- 2021.findings-emnlp.392
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4600–4609
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.392
- DOI:
- 10.18653/v1/2021.findings-emnlp.392
- Cite (ACL):
- Abhilash Nandy, Soumya Sharma, Shubham Maddhashiya, Kapil Sachdeva, Pawan Goyal, and NIloy Ganguly. 2021. Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4600–4609, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework (Nandy et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.findings-emnlp.392.pdf
- Code
- abhi1nandy2/emnlp-2021-findings
- Data
- E-Manual Corpus, TechQA