mlsanityData to 7 Oct 2026

A Comparative and Experimental Study on Automatic Question Answering Systems and its Robustness against Word Jumbling

arXiv 2311.15513 ↗First posted 27 Nov 2023cs.CL cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Question answer generation using Natural Language Processing models is ubiquitous in the world around us. It is used in many use cases such as the building of chat bots, suggestive prompts in google search and also as a way of navigating information in banking mobile applications etc. It is highly relevant because a frequently asked questions (FAQ) list can only have a finite amount of questions but a model which can perform question answer generation could be able to answer completely new questions that are within the scope of the data. This helps us to be able to answer new questions accurately as long as it is a relevant question. In commercial applications, it can be used to increase customer satisfaction and ease of usage. However a lot of data is generated by humans so it is susceptible to human error and this can adversely affect the model's performance and we are investigating this through our work

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 15 references that are in the map. First posted on arXiv 27 Nov 2023.

Builds on

9 of 15 references.

References · 9

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  4. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  5. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
  6. 2017Learning to Ask: Neural Question Generation for Reading Comprehensioncited by 186
  7. 2019Synthetic QA Corpora Generation with Roundtrip Consistencycited by 143
  8. 2019Generating Question-Answer Hierarchiescited by 20
  9. 2021An Automated Multiple-Choice Question Generation Using Natural Language Processing Techniquescited by 6

Built on by

Not cited yet.