Edison Marrese-Taylor, Yumi Hamazono, Tatsuya Ishigaki, Goran Topić, Yusuke Miyao, Ichiro Kobayashi, Hiroya Takamura
| Challenge: | Existing approaches to generate live commentary on specific domains have been limited. |
| Approach: | They propose to generate live commentary from transcribed videos in an open-domain setting . they propose to use well-known neural architectures to build models based on transcriptions . |
| Outcome: | The proposed model is based on well-known neural architectures and based off existing models. |
Similar Papers
Introducing Spatial Information and a Novel Evaluation Scheme for Open-Domain Live Commentary Generation (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Compared to domain-specific work in this task, this task proved particularly challenging due to the absence of domain- specific features. |
| Approach: | They propose an utterance generation model with a novel spatial graph that integrates spatial information to deal with the open-domain characteristics of the commentaries and significantly improves performance. |
| Outcome: | The proposed model significantly improves performance in the open-domain live commentary generation task. |
Learning to Generate Move-by-Move Commentary for Chess Games from Large-Scale Social Forum Data (P18-1)
Copied to clipboard
| Challenge: | Using a large-scale chess commentary dataset, we generate a set of comments for individual moves in a game. |
| Approach: | They propose a large-scale chess commentary dataset and a method to generate commentary for individual moves in a chessian game. |
| Outcome: | The proposed method is rated similar to ground truth commentary texts in terms of correctness and fluency. |
Automated Chess Commentator Powered by Neural Chess Engine (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to generate chess commentary are limited in template variety and are not precise enough. |
| Approach: | They propose a neural chess engine into text generation models to help with encoding boards, predicting moves, and analyzing situations. |
| Outcome: | The proposed model can be trained to generate chess commentary texts in 5 categories . the results are both automatic and human evaluations of the model . |
Event-Content-Oriented Dialogue Generation in Short Video (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing multi-modal dialogue models are limited to incapacity of reading visual information and multi-dimensional interactions. |
| Approach: | They propose a novel event-oriented video-dialogue dataset called SportsVD to overcome these challenges by generating human-like response according to event contents in the video and related external knowledge. |
| Outcome: | The proposed method outperforms existing methods on SportsVD and other baselines under several automatic metrics. |
LiveChat: A Large-Scale Personalized Dialogue Dataset Automatically Constructed from Live Streaming (2023.acl-long)
Copied to clipboard
| Challenge: | a recent study shows that open-domain dialogue systems are not able to perform well in fast-growing scenarios such as live streaming due to the domain gap between online-post constructed data and those required in downstream conversational tasks. |
| Approach: | They propose to train a conversational agent based on large social media datasets with multiple domains to improve response in live streaming scenarios. |
| Outcome: | The proposed model improves response modeling and addressee recognition in live open-domain scenarios. |
Generating Sports News from Live Commentary: A Chinese Dataset for Sports Game Summarization (2020.aacl-main)
Copied to clipboard
| Challenge: | Existing methods to generate sports summarization tasks are laborintensive and infeasible. |
| Approach: | They propose a Chinese dataset for sports game summarization and a model that consists of a selector and rewriter to evaluate the correctness of generated sports summaries. |
| Outcome: | The proposed model performs better on ROUGE and the two designed scores. |
Live Football Commentary System Providing Background Information (2025.acl-demo)
Copied to clipboard
Yuichiro Mori, Chikara Tanaka, Aru Maekawa, Satoshi Kosugi, Tatsuya Ishigaki, Kotaro Funakoshi, Hiroya Takamura, Manabu Okumura
| Challenge: | Existing studies on sports commentary generation focus on describing major events in the video, but real-world commentary often includes background information. |
| Approach: | They developed an audio commentary system that generates utterances with background information and play-by-play commentary for football matches. |
| Outcome: | The proposed system generates utterances with background information and play-by-play commentary for football matches. |
Commentary Generation from Data Records of Multiplayer Strategy Esports Game (2024.naacl-srw)
Copied to clipboard
| Challenge: | Esports play logs are expensive for human experts to provide individual games with play-by-play commentaries. |
| Approach: | They first build large-scale esports data-to-text datasets that pair structured data and commentaries from a popular eSports game, League of Legends. |
| Outcome: | The proposed model can generate game commentaries from esports’ data records while examining the impact of the pre-trained language models. |
AudioCaps: Generating Captions for Audios in The Wild (N19-1)
Copied to clipboard
| Challenge: | a dataset of 46K audio clips with human-written text pairs is used to generate captions for audio . the task of translating a multimedia input source into natural language has been extensively studied over the past few years . |
| Approach: | They propose a top-down multi-scale encoder and aligned semantic attention for audio captioning. |
| Outcome: | The proposed captions are faithful to audio inputs and better than existing models. |
MatchTime: Towards Automatic Soccer Game Commentary Generation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing data on soccer commentary are often unsatisfactory, and the quality of existing data is often poor. |
| Approach: | They propose to manually annotate timestamps for 49 soccer matches and then use them to create a model to correct and filter existing data. |
| Outcome: | The proposed model improves the viewing experience of soccer and can be trained on the curated dataset. |