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            <title>Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)</title>
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    <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&amp;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.</abstract>
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%0 Conference Proceedings
%T Truecasing German user-generated conversational text
%A Grishina, Yulia
%A Gueudre, Thomas
%A Winkler, Ralf
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F grishina-etal-2020-truecasing
%X 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.
%R 10.18653/v1/2020.wnut-1.19
%U https://aclanthology.org/2020.wnut-1.19
%U https://doi.org/10.18653/v1/2020.wnut-1.19
%P 143-148

Markdown (Informal)

[Truecasing German user-generated conversational text](https://aclanthology.org/2020.wnut-1.19) (Grishina et al., WNUT 2020)

• Truecasing German user-generated conversational text (Grishina et al., WNUT 2020)

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.