NeuralQA: A Usable Library for Question Answering (Contextual Query Expansion + BERT) on Large Datasets (2007.15211v2)

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 LLM (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).

• The paper introduces NeuralQA, a library that enhances QA performance on large datasets by integrating contextual query expansion and BERT.
• The paper implements CQE using a Masked Language Model and a specialized method, RelSnip, to condense long documents for efficient processing.
• The paper demonstrates an easy-to-integrate system with BM25 retrieval and gradient-based explanations, facilitating both research and enterprise adoption.

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

The paper discusses NeuralQA, a practical library aimed at enhancing Question Answering (QA) capabilities on extensive datasets, integrating technologies such as Contextual Query Expansion (CQE) and BERT. The development of NeuralQA addresses common challenges in existing QA systems, which often face difficulties related to integration complexity, lack of configurable interfaces, and incomplete support for the QA pipeline subtasks, including query expansion, retrieval, reading, and explanation.

Key innovations within NeuralQA include the use of CQE implemented with a Masked LLM (MLM) and a method named Relevant Snippets ($RelSnip$). $RelSnip$ effectively condenses lengthy documents into smaller, manageable passages, which optimize processing by document reader models. This is crucial since transformer-based architectures struggle with long sequences due to their self-attention mechanisms scaling quadratically, leading to high computational costs if documents require extensive chunk processing.

The library allows seamless integration with prevalent infrastructures like ElasticSearch and uses existing frameworks such as the HuggingFace Transformers API to train various reader models. A significant advantage is its flexible user interface, aiding both qualitative exploration in research settings and the deployment of large-scale search solutions. Additionally, NeuralQA provides visual tools for gradient-based explanations aiding users in debugging and understanding model behavior, thereby increasing system transparency and usability.

The QA Pipeline and System Architecture

NeuralQA enriches the QA pipeline by incorporating modular processes for Document Retrieval, Query Expansion, and Document Reading:

• Document Retrieval: NeuralQA employs BM25 to fetch candidate passages, supplemented by $RelSnip$ to align documents to suitable lengths for efficient reader processing.
• Contextual Query Expansion (CQE): The CQE module utilizes an MLM for generating contextually relevant query expansions, addressing vocabulary mismatches prevalent in sparse vector approaches, which can hinder retrieval effectiveness.
• Document Reading: Utilizing pretrained transformer models, the reader identifies and scores potential answer spans. It provides invaluable insights through gradient-based explanations, enhancing the model's interpretability.

Implications and Future Directions

NeuralQA has significant practical and theoretical implications. On the practical side, it serves as a robust tool for qualitative assessment of QA models across domains and enhances the deployment efficiency in enterprise settings. The theoretical contributions include advancing query expansion methodologies and optimizing document retrieval processes through techniques like $RelSnip$, which markedly improve processing speed while maintaining performance.

The potential future developments in the scope of AI that the paper indicates could include extending NeuralQA to support alternative retrieval mechanisms like Solr, implementing sophisticated explanation techniques, and extending CQE methodologies beyond current capabilities. The empirical evaluation of CQE and $RelSnip$ presents an opportunity to validate these methods further, potentially influencing future models seeking to balance efficiency and accuracy.

In conclusion, NeuralQA is a well-conceived library that aims to enhance the capabilities of QA systems, streamlining their deployment, usage, and understanding. Its focus on system usability and adaptability for various applications represents a step forward in scalable AI implementations, with promising avenues for further development and research.