# Ensemble approach for natural language question answering problem

arXiv: https://arxiv.org/abs/1908.09720 · HTML: https://mlsanity.com/paper/1908.09720/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2019.

## Built on by (most cited first, 7 of 7)

- [LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion](https://mlsanity.com/paper/2306.02561/) (2023)
- [Deep Model Fusion: A Survey](https://mlsanity.com/paper/2309.15698/) (2023)
- [EL4NER: Ensemble Learning for Named Entity Recognition via Multiple Small-Parameter Large Language Models](https://mlsanity.com/paper/2505.23038/) (2025)
- [Beyond Simple Averaging: Improving NLP Ensemble Performance with Topological-Data-Analysis-Based Weighting](https://mlsanity.com/paper/2402.14184/) (2024)
- [FuseRL: Dense Preference Optimization for Heterogeneous Model Fusion](https://mlsanity.com/paper/2504.06562/) (2025)
- [Zero-shot Task Transfer for Invoice Extraction via Class-aware QA Ensemble](https://mlsanity.com/paper/2108.06069/) (2021)
- [Advancing State of the Art in Language Modeling](https://mlsanity.com/paper/2312.03735/) (2023)

## Abstract (arXiv)

Machine comprehension, answering a question depending on a given context paragraph is a typical task of Natural Language Understanding. It requires to model complex dependencies existing between the question and the context paragraph. There are many neural network models attempting to solve the problem of question answering. The best models have been selected, studied and compared with each other. All the selected models are based on the neural attention mechanism concept. Additionally, studies on a SQUAD dataset were performed. The subsets of queries were extracted and then each model was analyzed how it deals with specific group of queries. Based on these three model ensemble model was created and tested on SQUAD dataset. It outperforms the best Mnemonic Reader model.