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        <title>Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging</title>
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    <abstract>Character-level models of tokens have been shown to be effective at dealing with within-token noise and out-of-vocabulary words. However, they often still rely on correct token boundaries. In this paper, we propose to eliminate the need for tokenizers with an end-to-end character-level semi-Markov conditional random field. It uses neural networks for its character and segment representations. We demonstrate its effectiveness in multilingual settings and when token boundaries are noisy: It matches state-of-the-art part-of-speech taggers for various languages and significantly outperforms them on a noisy English version of a benchmark dataset. Our code and the noisy dataset are publicly available at http://cistern.cis.lmu.de/semiCRF.</abstract>
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%0 Conference Proceedings
%T Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging
%A Kemos, Apostolos
%A Adel, Heike
%A Schütze, Hinrich
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F kemos-etal-2019-neural
%X Character-level models of tokens have been shown to be effective at dealing with within-token noise and out-of-vocabulary words. However, they often still rely on correct token boundaries. In this paper, we propose to eliminate the need for tokenizers with an end-to-end character-level semi-Markov conditional random field. It uses neural networks for its character and segment representations. We demonstrate its effectiveness in multilingual settings and when token boundaries are noisy: It matches state-of-the-art part-of-speech taggers for various languages and significantly outperforms them on a noisy English version of a benchmark dataset. Our code and the noisy dataset are publicly available at http://cistern.cis.lmu.de/semiCRF.
%R 10.18653/v1/N19-1280
%U https://aclanthology.org/N19-1280/
%U https://doi.org/10.18653/v1/N19-1280
%P 2736-2743

Markdown (Informal)

[Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging](https://aclanthology.org/N19-1280/) (Kemos et al., NAACL 2019)

• Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging (Kemos et al., NAACL 2019)

ACL

• Apostolos Kemos, Heike Adel, and Hinrich Schütze. 2019. Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2736–2743, Minneapolis, Minnesota. Association for Computational Linguistics.