Generating Live Soccer-Match Commentary from Play Data

By Yasufumi Taniguchi, Yukun Feng, Hiroya Takamura, Manabu Okumura. 2019

32 citations recorded by the source corpus.

We address the task of generating live soccer-match commentaries from play event data. This task has characteristics that (i) each commentary is only partially aligned with events, (ii) play event data contains many types of categorical and numerical attributes, (iii) live commentaries often mention player names and team names. For these reasons, we propose an encoder for play event data, which is enhanced with a gate mechanism. We also introduce an attention mechanism on events. In addition, we introduced placeholders and their reconstruction mechanism to enable the model to copy appropriate player names and team names from the input data. We conduct experiments on the play data of the English Premier League, provide a discussion on the result including generated commentaries.

Subfields: Computer Vision and Pattern Recognition, Economics and Econometrics, Sociology and Political Science, Natural Language Generation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Caiming Xiong
2. Richard Socher
3. Luc Van Gool
4. Trevor Darrell
5. Nazneen Rajani
6. Dacheng Tao
7. Cordelia Schmid
8. Xiaogang Wang

In Economics and Econometrics

1. Caiming Xiong

In Sociology and Political Science

1. Y. Tan
2. Richard Socher
3. Caiming Xiong
4. Dragomir R. Radev

In Natural Language Generation

1. Rui Zhang
2. Richard Socher
3. Caiming Xiong
4. Dragomir R. Radev
5. Craig Thomson
6. Ehud Reiter

Citations in the corpus

• DART: Open-Domain Structured Data Record to Text Generation (2020, background): “Applications include generating sports commentaries (Tanaka-Ishii et al., 1998; Chen and Mooney, 2008; Wiseman et al., 2017; Wang, 2019; Taniguchi et al., 2019), weather forecasts (Goldberg et al., 1994; Reiter et al., 2005; Liang et al., 2009; Konstas and Lapata, 2012; Mei et al., 2016),…”
• Learning to Select, Track, and Generate for Data-to-Text (2019, methodology): “1 Data-to-Text Generation Data-to-text generation is a task for generating descriptions from structured or non-structured data including sports commentary (Tanaka-Ishii et al., 1998; Chen and Mooney, 2008; Taniguchi et al., 2019), weather forecast (Liang et al.”
• ESPRIT: Explaining Solutions to Physical Reasoning Tasks (2020, background): “Applications include generating sports commentaries from game records (Tanaka-Ishii et al., 1998; Chen and Mooney, 2008; Taniguchi et al., 2019), weather forecasts (Liang et al.”