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

TL;DR

NeuralQA is an open-source library that simplifies question answering on large datasets by integrating query expansion, document condensation, and flexible interfaces, improving usability and functionality over existing tools.

Contribution

It introduces contextual query expansion and relevant snippet methods, along with a flexible interface, enhancing QA pipeline coverage and usability in practical settings.

Findings

Supports integration with ElasticSearch and HuggingFace models

Provides methods for query expansion and document condensation

Includes visualization tools for model explanations

Abstract

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).