Abstract
The recognition and automatic annotation of temporal expressions (e.g. “Add an event for tomorrow evening at eight to my calendar”) is a key module for AI voice assistants, in order to allow them to interact with apps (for example, a calendar app). However, in the NLP literature, research on temporal expressions has focused mostly on data from the news, from the clinical domain, and from social media. The voice assistant domain is very different than the typical domains that have been the focus of work on temporal expression identification, thus requiring a dedicated data collection. We present a crowdsourcing method for eliciting natural-language commands containing temporal expressions for an AI voice assistant, by using pictures and scenario descriptions. We annotated the elicited commands (480) as well as the commands in the Snips dataset following the TimeML/TIMEX3 annotation guidelines, reaching a total of 1188 annotated commands. The commands can be later used to train the NLU components of an AI voice assistant.- Anthology ID:
- 2020.lrec-1.66
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 523–530
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.66
- DOI:
- Cite (ACL):
- Alessandra Zarcone, Touhidul Alam, and Zahra Kolagar. 2020. PATE: A Corpus of Temporal Expressions for the In-car Voice Assistant Domain. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 523–530, Marseille, France. European Language Resources Association.
- Cite (Informal):
- PATE: A Corpus of Temporal Expressions for the In-car Voice Assistant Domain (Zarcone et al., LREC 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2020.lrec-1.66.pdf