Abstract
True-casing, the task of restoring proper case to (generally) lower case input, is important in downstream tasks and for screen display. In this paper, we investigate truecasing as an in- trinsic task and present several experiments on noisy user queries to a voice-controlled dia- log system. In particular, we compare a rule- based, an n-gram language model (LM) and a recurrent neural network (RNN) approaches, evaluating the results on a German Q&A cor- pus and reporting accuracy for different case categories. We show that while RNNs reach higher accuracy especially on large datasets, character n-gram models with interpolation are still competitive, in particular on mixed- case words where their fall-back mechanisms come into play.- Anthology ID:
- 2020.wnut-1.19
- Volume:
- Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
- Venue:
- WNUT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 143–148
- Language:
- URL:
- https://aclanthology.org/2020.wnut-1.19
- DOI:
- 10.18653/v1/2020.wnut-1.19
- Cite (ACL):
- Yulia Grishina, Thomas Gueudre, and Ralf Winkler. 2020. Truecasing German user-generated conversational text. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 143–148, Online. Association for Computational Linguistics.
- Cite (Informal):
- Truecasing German user-generated conversational text (Grishina et al., WNUT 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.wnut-1.19.pdf