# Discourse Parsing in Videos: A Multi-modal Appraoch

## TL;DR

This paper introduces the task of visual discourse parsing in videos, proposing a method to identify discourse cues without explicit scene annotation, supported by a new dataset of 310 videos.

## Contribution

It presents a novel approach to extract discourse cues from videos without scene annotation and provides a new dataset for this task.

## Key findings

- Successfully identified discourse cues without scene annotation
- Created a dataset with 310 videos for visual discourse parsing
- Potential applications in Visual Dialog and Visual Storytelling

## Abstract

Text-level discourse parsing aims to unmask how two sentences in the text are related to each other. We propose the task of Visual Discourse Parsing, which requires understanding discourse relations among scenes in a video. Here we use the term scene to refer to a subset of video frames that can better summarize the video. In order to collect a dataset for learning discourse cues from videos, one needs to manually identify the scenes from a large pool of video frames and then annotate the discourse relations between them. This is clearly a time consuming, expensive and tedious task. In this work, we propose an approach to identify discourse cues from the videos without the need to explicitly identify and annotate the scenes. We also present a novel dataset containing 310 videos and the corresponding discourse cues to evaluate our approach. We believe that many of the multi-discipline AI problems such as Visual Dialog and Visual Storytelling would greatly benefit from the use of visual discourse cues.