Christian Schallhart


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Data-Driven Parametric Text Normalization: Rapidly Scaling Finite-State Transduction Verbalizers to New Languages
Sandy Ritchie | Eoin Mahon | Kim Heiligenstein | Nikos Bampounis | Daan van Esch | Christian Schallhart | Jonas Mortensen | Benoit Brard
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)

This paper presents a methodology for rapidly generating FST-based verbalizers for ASR and TTS systems by efficiently sourcing language-specific data. We describe a questionnaire which collects the necessary data to bootstrap the number grammar induction system and parameterize the verbalizer templates described in Ritchie et al. (2019), and a machine-readable data store which allows the data collected through the questionnaire to be supplemented by additional data from other sources. This system allows us to rapidly scale technologies such as ASR and TTS to more languages, including low-resource languages.

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Frugal Paradigm Completion
Alexander Erdmann | Tom Kenter | Markus Becker | Christian Schallhart
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Lexica distinguishing all morphologically related forms of each lexeme are crucial to many language technologies, yet building them is expensive. We propose a frugal paradigm completion approach that predicts all related forms in a morphological paradigm from as few manually provided forms as possible. It induces typological information during training which it uses to determine the best sources at test time. We evaluate our language-agnostic approach on 7 diverse languages. Compared to popular alternative approaches, ours reduces manual labor by 16-63% and is the most robust to typological variation.


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EAGER: Extending Automatically Gazetteers for Entity Recognition
Omer Farukhan Gunes | Tim Furche | Christian Schallhart | Jens Lehmann | Axel-Cyrille Ngonga Ngomo
Proceedings of the 3rd Workshop on the People’s Web Meets NLP: Collaboratively Constructed Semantic Resources and their Applications to NLP