# A Question-Focused Multi-Factor Attention Network for Question Answering

arXiv: https://arxiv.org/abs/1801.08290 · HTML: https://mlsanity.com/paper/1801.08290/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2018.

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

- [PQuAD: A Persian Question Answering Dataset](https://mlsanity.com/paper/2202.06219/) (2022)
- [Evaluating ChatGPT as a Question Answering System: A Comprehensive Analysis and Comparison with Existing Models](https://mlsanity.com/paper/2312.07592/) (2023)
- [MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction](https://mlsanity.com/paper/2401.09839/) (2024)
- [Sparse-softmax: A Simpler and Faster Alternative Softmax Transformation](https://mlsanity.com/paper/2112.12433/) (2021)
- [tagE: Enabling an Embodied Agent to Understand Human Instructions](https://mlsanity.com/paper/2310.15605/) (2023)
- [Metaknowledge Extraction Based on Multi-Modal Documents](https://mlsanity.com/paper/2102.02971/) (2021)
- [An Automated Question-Answering Framework Based on Evolution Algorithm](https://mlsanity.com/paper/2201.10797/) (2022)

## Abstract (arXiv)

Neural network models recently proposed for question answering (QA) primarily focus on capturing the passage-question relation. However, they have minimal capability to link relevant facts distributed across multiple sentences which is crucial in achieving deeper understanding, such as performing multi-sentence reasoning, co-reference resolution, etc. They also do not explicitly focus on the question and answer type which often plays a critical role in QA. In this paper, we propose a novel end-to-end question-focused multi-factor attention network for answer extraction. Multi-factor attentive encoding using tensor-based transformation aggregates meaningful facts even when they are located in multiple sentences. To implicitly infer the answer type, we also propose a max-attentional question aggregation mechanism to encode a question vector based on the important words in a question. During prediction, we incorporate sequence-level encoding of the first wh-word and its immediately following word as an additional source of question type information. Our proposed model achieves significant improvements over the best prior state-of-the-art results on three large-scale challenging QA datasets, namely NewsQA, TriviaQA, and SearchQA.