@inproceedings{chang-etal-2022-logic,
title = "Logic-Guided Message Generation from Raw Real-Time Sensor Data",
author = "Chang, Ernie and
Kovtunova, Alisa and
Borgwardt, Stefan and
Demberg, Vera and
Chapman, Kathryn and
Yeh, Hui-Syuan",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.745",
pages = "6899--6908",
abstract = "Natural language generation in real-time settings with raw sensor data is a challenging task. We find that formulating the task as an end-to-end problem leads to two major challenges in content selection {--} the sensor data is both redundant and diverse across environments, thereby making it hard for the encoders to select and reason on the data. We here present a new corpus for a specific domain that instantiates these properties. It includes handover utterances that an assistant for a semi-autonomous drone uses to communicate with humans during the drone flight. The corpus consists of sensor data records and utterances in 8 different environments. As a structured intermediary representation between data records and text, we explore the use of description logic (DL). We also propose a neural generation model that can alert the human pilot of the system state and environment in preparation of the handover of control.",
}
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%0 Conference Proceedings
%T Logic-Guided Message Generation from Raw Real-Time Sensor Data
%A Chang, Ernie
%A Kovtunova, Alisa
%A Borgwardt, Stefan
%A Demberg, Vera
%A Chapman, Kathryn
%A Yeh, Hui-Syuan
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F chang-etal-2022-logic
%X Natural language generation in real-time settings with raw sensor data is a challenging task. We find that formulating the task as an end-to-end problem leads to two major challenges in content selection – the sensor data is both redundant and diverse across environments, thereby making it hard for the encoders to select and reason on the data. We here present a new corpus for a specific domain that instantiates these properties. It includes handover utterances that an assistant for a semi-autonomous drone uses to communicate with humans during the drone flight. The corpus consists of sensor data records and utterances in 8 different environments. As a structured intermediary representation between data records and text, we explore the use of description logic (DL). We also propose a neural generation model that can alert the human pilot of the system state and environment in preparation of the handover of control.
%U https://aclanthology.org/2022.lrec-1.745
%P 6899-6908
Markdown (Informal)
[Logic-Guided Message Generation from Raw Real-Time Sensor Data](https://aclanthology.org/2022.lrec-1.745) (Chang et al., LREC 2022)
ACL
- Ernie Chang, Alisa Kovtunova, Stefan Borgwardt, Vera Demberg, Kathryn Chapman, and Hui-Syuan Yeh. 2022. Logic-Guided Message Generation from Raw Real-Time Sensor Data. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6899–6908, Marseille, France. European Language Resources Association.
