# Multi-step Joint-Modality Attention Network for Scene-Aware Dialogue System

arXiv: https://arxiv.org/abs/2001.06206 · HTML: https://mlsanity.com/paper/2001.06206/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2020.

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

- [Information-Theoretic Text Hallucination Reduction for Video-grounded Dialogue](https://mlsanity.com/paper/2212.05765/) (2022)
- [Structured Co-reference Graph Attention for Video-grounded Dialogue](https://mlsanity.com/paper/2103.13361/) (2021)
- [HEAR: Hearing Enhanced Audio Response for Video-grounded Dialogue](https://mlsanity.com/paper/2312.09736/) (2023)
- [Learning Reasoning Paths over Semantic Graphs for Video-grounded Dialogues](https://mlsanity.com/paper/2103.00820/) (2021)
- [VGNMN: Video-grounded Neural Module Network to Video-Grounded Language Tasks](https://mlsanity.com/paper/2104.07921/) (2021)
- [Video Dialog as Conversation about Objects Living in Space-Time](https://mlsanity.com/paper/2207.03656/) (2022)
- [End-to-End Multimodal Representation Learning for Video Dialog](https://mlsanity.com/paper/2210.14512/) (2022)
- [Multi-Modal Video Dialog State Tracking in the Wild](https://mlsanity.com/paper/2407.02218/) (2024)
- [C3: Compositional Counterfactual Contrastive Learning for Video-grounded Dialogues](https://mlsanity.com/paper/2106.08914/) (2021)
- [Uncovering Hidden Connections: Iterative Search and Reasoning for Video-grounded Dialog](https://mlsanity.com/paper/2310.07259/) (2023)
- [V2Dial: Unification of Video and Visual Dialog via Multimodal Experts](https://mlsanity.com/paper/2503.02063/) (2025)

## Abstract (arXiv)

Understanding dynamic scenes and dialogue contexts in order to converse with users has been challenging for multimodal dialogue systems. The 8-th Dialog System Technology Challenge (DSTC8) proposed an Audio Visual Scene-Aware Dialog (AVSD) task, which contains multiple modalities including audio, vision, and language, to evaluate how dialogue systems understand different modalities and response to users. In this paper, we proposed a multi-step joint-modality attention network (JMAN) based on recurrent neural network (RNN) to reason on videos. Our model performs a multi-step attention mechanism and jointly considers both visual and textual representations in each reasoning process to better integrate information from the two different modalities. Compared to the baseline released by AVSD organizers, our model achieves a relative 12.1% and 22.4% improvement over the baseline on ROUGE-L score and CIDEr score.