# Visual Question Rewriting for Increasing Response Rate

arXiv: https://arxiv.org/abs/2106.02257 · HTML: https://mlsanity.com/paper/2106.02257/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 24 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [Modulating early visual processing by language](https://mlsanity.com/paper/1707.00683/) (2017)
- [From Captions to Visual Concepts and Back](https://mlsanity.com/paper/1411.4952/) (2014)
- [Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation](https://mlsanity.com/paper/1904.02357/) (2019)
- [ALTER: Auxiliary Text Rewriting Tool for Natural Language Generation](https://mlsanity.com/paper/1909.06564/) (2019)

## Built on by (most cited first, 3 of 3)

- [ReVision: A Dataset and Baseline VLM for Privacy-Preserving Task-Oriented Visual Instruction Rewriting](https://mlsanity.com/paper/2502.14780/) (2025)
- [Multi-VQG: Generating Engaging Questions for Multiple Images](https://mlsanity.com/paper/2211.07441/) (2022)
- [MAPWise: Evaluating Vision-Language Models for Advanced Map Queries](https://mlsanity.com/paper/2409.00255/) (2024)

## Abstract (arXiv)

When a human asks questions online, or when a conversational virtual agent asks human questions, questions triggering emotions or with details might more likely to get responses or answers. we explore how to automatically rewrite natural language questions to improve the response rate from people. In particular, a new task of Visual Question Rewriting(VQR) task is introduced to explore how visual information can be used to improve the new questions. A data set containing around 4K bland questions, attractive questions and images triples is collected. We developed some baseline sequence to sequence models and more advanced transformer based models, which take a bland question and a related image as input and output a rewritten question that is expected to be more attractive. Offline experiments and mechanical Turk based evaluations show that it is possible to rewrite bland questions in a more detailed and attractive way to increase the response rate, and images can be helpful.