%0 Conference Proceedings %T New Domain, Major Effort? How Much Data is Necessary to Adapt a Temporal Tagger to the Voice Assistant Domain %A Alam, Touhidul %A Zarcone, Alessandra %A Padó, Sebastian %Y Zarrieß, Sina %Y Bos, Johan %Y van Noord, Rik %Y Abzianidze, Lasha %S Proceedings of the 14th International Conference on Computational Semantics (IWCS) %D 2021 %8 June %I Association for Computational Linguistics %C Groningen, The Netherlands (online) %F alam-etal-2021-new %X Reliable tagging of Temporal Expressions (TEs, e.g., Book a table at L’Osteria for Sunday evening) is a central requirement for Voice Assistants (VAs). However, there is a dearth of resources and systems for the VA domain, since publicly-available temporal taggers are trained only on substantially different domains, such as news and clinical text. Since the cost of annotating large datasets is prohibitive, we investigate the trade-off between in-domain data and performance in DA-Time, a hybrid temporal tagger for the English VA domain which combines a neural architecture for robust TE recognition, with a parser-based TE normalizer. We find that transfer learning goes a long way even with as little as 25 in-domain sentences: DA-Time performs at the state of the art on the news domain, and substantially outperforms it on the VA domain. %U https://aclanthology.org/2021.iwcs-1.14 %P 144-154