# Gated-Attention Readers for Text Comprehension

arXiv: https://arxiv.org/abs/1606.01549 · HTML: https://mlsanity.com/paper/1606.01549/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2016.

## Built on by (most cited first, 30 of 37)

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- [GreenMind: A Next-Generation Vietnamese Large Language Model for Structured and Logical Reasoning](https://mlsanity.com/paper/2504.16832/) (2025)
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- [Audio-Oriented Multimodal Machine Comprehension: Task, Dataset and Model](https://mlsanity.com/paper/2107.01571/) (2021)
- [A Comprehensive Survey on Multi-hop Machine Reading Comprehension Datasets and Metrics](https://mlsanity.com/paper/2212.04070/) (2022)
- [A Comprehensive Survey on Multi-hop Machine Reading Comprehension Approaches](https://mlsanity.com/paper/2212.04072/) (2022)
- [Graph Attention with Hierarchies for Multi-hop Question Answering](https://mlsanity.com/paper/2301.11792/) (2023)
- [Orthogonal Attention: A Cloze-Style Approach to Negation Scope Resolution](https://mlsanity.com/paper/2103.04294/) (2021)
- [ReCAM@IITK at SemEval-2021 Task 4: BERT and ALBERT based Ensemble for Abstract Word Prediction](https://mlsanity.com/paper/2104.01563/) (2021)
- [Automatic Task Requirements Writing Evaluation via Machine Reading Comprehension](https://mlsanity.com/paper/2107.07957/) (2021)

## Abstract (arXiv)

In this paper we study the problem of answering cloze-style questions over documents. Our model, the Gated-Attention (GA) Reader, integrates a multi-hop architecture with a novel attention mechanism, which is based on multiplicative interactions between the query embedding and the intermediate states of a recurrent neural network document reader. This enables the reader to build query-specific representations of tokens in the document for accurate answer selection. The GA Reader obtains state-of-the-art results on three benchmarks for this task--the CNN & Daily Mail news stories and the Who Did What dataset. The effectiveness of multiplicative interaction is demonstrated by an ablation study, and by comparing to alternative compositional operators for implementing the gated-attention. The code is available at https://github.com/bdhingra/ga-reader.