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Neural text normalization with adapted decoding and POS features

Published online by Cambridge University Press:  20 August 2019

T. Ruzsics ,
M. Lusetti ,
A. Göhring ,
T. Samardžić Open the ORCID record for T. Samardžić [Opens in a new window]  and
E. Stark
T. Ruzsics*
Affiliation:
URPP Language and Space, University of Zurich, Zurich, Switzerland
M. Lusetti
Affiliation:
Institute of Romance Studies, University of Zurich, Zurich, Switzerland
A. Göhring
Affiliation:
Institute of Romance Studies, University of Zurich, Zurich, Switzerland Institute of Computational Linguistics, University of Zurich, Zurich, Switzerland
T. Samardžić
Affiliation:
URPP Language and Space, University of Zurich, Zurich, Switzerland
E. Stark
Affiliation:
Institute of Romance Studies, University of Zurich, Zurich, Switzerland
*
*Corresponding author. Email: tatiana.ruzsics@uzh.ch
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Abstract

Text normalization is the task of mapping noncanonical language, typical of speech transcription and computer-mediated communication, to a standardized writing. This task is especially important for languages such as Swiss German, with strong regional variation and no written standard. In this paper, we propose a novel solution for normalizing Swiss German WhatsApp messages using the encoder–decoder neural machine translation (NMT) framework. We enhance the performance of a plain character-level NMT model with the integration of a word-level language model and linguistic features in the form of part-of-speech (POS) tags. The two components are intended to improve the performance by addressing two specific issues: the former is intended to improve the fluency of the predicted sequences, whereas the latter aims at resolving cases of word-level ambiguity. Our systematic comparison shows that our proposed solution results in an improvement over a plain NMT system and also over a comparable character-level statistical machine translation system, considered the state of the art in this task till recently. We perform a thorough analysis of the compared systems’ output, showing that our two components produce indeed the intended, complementary improvements.

Keywords

Text normalizationNeural machine translationSwiss German

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Footnotes

*

This research is funded by the Swiss National Science Foundation, project “What’s Up, Switzerland? Language, Individuals and Ideologies in Mobile Messaging” (Sinergia: CRSII1_160714).

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