PASS: A Dutch data-to-text system for soccer, targeted towards specific audiences

Abstract

We present PASS, a data-to-text system that generates Dutch soccer reports from match statistics. One of the novel elements of PASS is the fact that the system produces corpusbased texts tailored towards fans of one club or the other, which can most prominently be observed in the tone of voice used in the reports. Furthermore, the system is open source and uses a modular design, which makes it relatively easy for people to add extensions. Human-based evaluation shows that people are generally positive towards PASS in regards to its clarity and fluency, and that the tailoring is accurately recognized in most cases.

A Closer Look at Recent Results of Verb Selection for Data-to-Text NLG

“…In this study, we focus on a few issues with recent work on trend verb selection in describing various kinds of percentages, with experiments conducted on samples collected from financial news data. We believe that similar caveats should exist in lexical choice problems appeared in other domains as well, such as various kinds of phrases in weather forecasts (Reiter et al, 2005;Ramos-Soto et al, 2014;Li et al, 2016) and in sports match reports (van der Lee et al, 2017;Wiseman et al, 2017;Qin et al, 2018), and hopefully the field will be exploring on some more principled, domain-agnostic approaches in the future.…”

A Personalized Data-to-Text Support Tool for Cancer Patients

“…For the development of this tool, we build on the PASS data-to-text system (van der Lee et al, 2017), which was originally developed for the tailored generation of soccer reports. Building on an analysis of existing decision aids (Vromans et al, 2019a,b), templates were defined with different framing policies relating to different outcome scenarios.…”

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

“…For example, data from babies in neonatal care can be converted into text differently, with different levels of technical detail and explanatory language, depending on whether the intended reader is a doctor, a nurse or a parent (Mahamood & Reiter, 2011). One could also easily imagine that different sport reports are generated for fans of the respective teams; the winning goal of one team is likely to be considered a lucky one from the perspective of the losing team, irrespective of its 'objective' qualities (van der Lee et al, 2017). A human journalist would not dream of writing separate reports about a sports match (if only for lack of time), but for a computer this is not an issue and this is likely to be appreciated by a reader who receives a more personally appropriate report.…”