# Knowledge Enhanced Model for Live Video Comment Generation

arXiv: https://arxiv.org/abs/2304.14657 · HTML: https://mlsanity.com/paper/2304.14657/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 22 references that are in the map.
- First posted on arXiv 28 Apr 2023.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
- [K-BERT: Enabling Language Representation with Knowledge Graph](https://mlsanity.com/paper/1909.07606/) (2019)
- [UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation](https://mlsanity.com/paper/2002.06353/) (2020)
- [KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA](https://mlsanity.com/paper/2012.11014/) (2020)
- [CoLAKE: Contextualized Language and Knowledge Embedding](https://mlsanity.com/paper/2010.00309/) (2020)
- [KdConv: A Chinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation](https://mlsanity.com/paper/2004.04100/) (2020)
- [VIOLIN: A Large-Scale Dataset for Video-and-Language Inference](https://mlsanity.com/paper/2003.11618/) (2020)
- [Movie Plot Analysis via Turning Point Identification](https://mlsanity.com/paper/1908.10328/) (2019)
- [Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation](https://mlsanity.com/paper/2110.08501/) (2021)
- [LiveBot: Generating Live Video Comments Based on Visual and Textual Contexts](https://mlsanity.com/paper/1809.04938/) (2018)
- [DCA: Diversified Co-Attention towards Informative Live Video Commenting](https://mlsanity.com/paper/1911.02739/) (2019)
- [Response to LiveBot: Generating Live Video Comments Based on Visual and Textual Contexts](https://mlsanity.com/paper/2006.03022/) (2020)
- [Multimodal Matching Transformer for Live Commenting](https://mlsanity.com/paper/2002.02649/) (2020)

## Built on by (most cited first, 6 of 6)

- [Movie101v2: Improved Movie Narration Benchmark](https://mlsanity.com/paper/2404.13370/) (2024)
- [HOTVCOM: Generating Buzzworthy Comments for Videos](https://mlsanity.com/paper/2409.15196/) (2024)
- [HotComment: A Benchmark for Evaluating Popularity of Online Comments](https://mlsanity.com/paper/2604.25614/) (2026)
- [InteracVid: Building a Real Interactive Audio-Visual Response Dataset from Live-Chat Videos](https://mlsanity.com/paper/2608.01157/) (2026)
- [Enhancing Multimodal Affective Analysis with Learned Live Comment Features](https://mlsanity.com/paper/2410.16407/) (2024)
- [Semantic Frame Aggregation-based Transformer for Live Video Comment Generation](https://mlsanity.com/paper/2510.26978/) (2025)

## Abstract (arXiv)

Live video commenting is popular on video media platforms, as it can create a chatting atmosphere and provide supplementary information for users while watching videos. Automatically generating live video comments can improve user experience and enable human-like generation for bot chatting. Existing works mostly focus on short video datasets while ignoring other important video types such as long videos like movies. In this work, we collect a new Movie Live Comments (MovieLC) dataset to support research on live video comment generation for long videos. We also propose a knowledge enhanced generation model inspired by the divergent and informative nature of live video comments. Our model adopts a pre-training encoder-decoder framework and incorporates external knowledge. Extensive experiments show that both objective metrics and human evaluation demonstrate the effectiveness of our proposed model. The MovieLC dataset and our code will be released.