# Techniques to Improve Q&A Accuracy with Transformer-based models on Large Complex Documents

arXiv: https://arxiv.org/abs/2009.12695 · HTML: https://mlsanity.com/paper/2009.12695/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2020.

## Built on by (most cited first, 1 of 1)

- [Life of PII -- A PII Obfuscation Transformer](https://mlsanity.com/paper/2305.09550/) (2023)

## Abstract (arXiv)

This paper discusses the effectiveness of various text processing techniques, their combinations, and encodings to achieve a reduction of complexity and size in a given text corpus. The simplified text corpus is sent to BERT (or similar transformer based models) for question and answering and can produce more relevant responses to user queries. This paper takes a scientific approach to determine the benefits and effectiveness of various techniques and concludes a best-fit combination that produces a statistically significant improvement in accuracy.