# A Gated Self-attention Memory Network for Answer Selection

arXiv: https://arxiv.org/abs/1909.09696 · HTML: https://mlsanity.com/paper/1909.09696/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2019.

## Built on by (most cited first, 11 of 11)

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- [LRG at SemEval-2021 Task 4: Improving Reading Comprehension with Abstract Words using Augmentation, Linguistic Features and Voting](https://mlsanity.com/paper/2102.12255/) (2021)
- [Combining Transformers with Natural Language Explanations](https://mlsanity.com/paper/2110.00125/) (2021)
- [LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity Recognition](https://mlsanity.com/paper/2203.03546/) (2022)
- [Hierarchical Ranking for Answer Selection](https://mlsanity.com/paper/2102.00677/) (2021)
- [Long-Term Prediction Accuracy Improvement of Data-Driven Medium-Range Global Weather Forecast](https://mlsanity.com/paper/2407.01598/) (2024)

## Abstract (arXiv)

Answer selection is an important research problem, with applications in many areas. Previous deep learning based approaches for the task mainly adopt the Compare-Aggregate architecture that performs word-level comparison followed by aggregation. In this work, we take a departure from the popular Compare-Aggregate architecture, and instead, propose a new gated self-attention memory network for the task. Combined with a simple transfer learning technique from a large-scale online corpus, our model outperforms previous methods by a large margin, achieving new state-of-the-art results on two standard answer selection datasets: TrecQA and WikiQA.