# NeuralQA: A Usable Library for Question Answering (Contextual Query Expansion + BERT) on Large Datasets

arXiv: https://arxiv.org/abs/2007.15211 · HTML: https://mlsanity.com/paper/2007.15211/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [UKP-SQUARE: An Online Platform for Question Answering Research](https://mlsanity.com/paper/2203.13693/) (2022)
- [Axiomatic Preference Modeling for Longform Question Answering](https://mlsanity.com/paper/2312.02206/) (2023)
- [FACOS: Finding API Relevant Contents on Stack Overflow with Semantic and Syntactic Analysis](https://mlsanity.com/paper/2111.07238/) (2021)

## Abstract (arXiv)

Existing tools for Question Answering (QA) have challenges that limit their use in practice. They can be complex to set up or integrate with existing infrastructure, do not offer configurable interactive interfaces, and do not cover the full set of subtasks that frequently comprise the QA pipeline (query expansion, retrieval, reading, and explanation/sensemaking). To help address these issues, we introduce NeuralQA - a usable library for QA on large datasets. NeuralQA integrates well with existing infrastructure (e.g., ElasticSearch instances and reader models trained with the HuggingFace Transformers API) and offers helpful defaults for QA subtasks. It introduces and implements contextual query expansion (CQE) using a masked language model (MLM) as well as relevant snippets (RelSnip) - a method for condensing large documents into smaller passages that can be speedily processed by a document reader model. Finally, it offers a flexible user interface to support workflows for research explorations (e.g., visualization of gradient-based explanations to support qualitative inspection of model behaviour) and large scale search deployment. Code and documentation for NeuralQA is available as open source on Github (https://github.com/victordibia/neuralqa}{Github).