Proceedings of the 15th International Conference on Natural Language Generation: System Demonstrations

Samira Shaikh, Thiago Ferreira, Amanda Stent (Editors)


Anthology ID:
2022.inlg-demos
Month:
July
Year:
2022
Address:
Waterville, Maine, USA and virtual meeting
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
URL:
https://aclanthology.org/2022.inlg-demos
DOI:
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
https://aclanthology.org/2022.inlg-demos.pdf

pdf bib
Proceedings of the 15th International Conference on Natural Language Generation: System Demonstrations
Samira Shaikh | Thiago Ferreira | Amanda Stent

pdf bib
BLAB Reporter: Automated journalism covering the Blue Amazon
Yan Sym | João Campos | Fabio Cozman

This demo paper introduces BLAB reporter, a robot-journalist system covering the Brazilian Blue Amazon. The application is based on a pipeline architecture for Natural Language Generation, which offers daily reports, news summaries and curious facts in Brazilian Portuguese. By collecting, storing and analysing structured data from publicly available sources, the robot-journalist uses domain knowledge to generate, validate and publish texts in Twitter. Code and corpus are publicly available.

pdf bib
Generating Quizzes to Support Training on Quality Management and Assurance in Space Science and Engineering
Andres Garcia-Silva | Cristian Berrio Aroca | Jose Manuel Gomez-Perez | Jose Martinez | Patrick Fleith | Stefano Scaglioni

Quality management and assurance is key for space agencies to guarantee the success of space missions, which are high-risk and extremely costly. In this paper, we present a system to generate quizzes, a common resource to evaluate the effectiveness of training sessions, from documents about quality assurance procedures in the Space domain. Our system leverages state of the art auto-regressive models like T5 and BART to generate questions, and a RoBERTa model to extract answers for such questions, thus verifying their suitability.

pdf bib
Automated Ad Creative Generation
Vishakha Kadam | Yiping Jin | Bao-Dai Nguyen-Hoang

Ad creatives are ads served to users on a webpage, app, or other digital environments. The demand for compelling ad creatives surges drastically with the ever-increasing popularity of digital marketing. The two most essential elements of (display) ad creatives are the advertising message, such as headlines and description texts, and the visual component, such as images and videos. Traditionally, ad creatives are composed by professional copywriters and creative designers. The process requires significant human effort, limiting the scalability and efficiency of digital ad campaigns. This work introduces AUTOCREATIVE, a novel system to automatically generate ad creatives relying on natural language generation and computer vision techniques. The system generates multiple ad copies (ad headlines/description texts) using a sequence-to-sequence model and selects images most suitable to the generated ad copies based on heuristic-based visual appeal metrics and a text-image retrieval pipeline.

pdf bib
THEaiTRobot: An Interactive Tool for Generating Theatre Play Scripts
Rudolf Rosa | Patrícia Schmidtová | Alisa Zakhtarenko | Ondrej Dusek | Tomáš Musil | David Mareček | Saad Ul Islam | Marie Novakova | Klara Vosecka | Daniel Hrbek | David Kostak

We present a free online demo of THEaiTRobot, an open-source bilingual tool for interactively generating theatre play scripts, in two versions. THEaiTRobot 1.0 uses the GPT-2 language model with minimal adjustments. THEaiTRobot 2.0 uses two models created by fine-tuning GPT-2 on purposefully collected and processed datasets and several other components, generating play scripts in a hierarchical fashion (title synopsis script). The underlying tool is used in the THEaiTRE project to generate scripts for plays, which are then performed on stage by a professional theatre.