Comparing MT Approaches for Text Normalization

Claudia Matos Veliz, Orphee De Clercq, Veronique Hoste

Abstract
One of the main characteristics of social media data is the use of non-standard language. Since NLP tools have been trained on traditional text material their performance drops when applied to social media data. One way to overcome this is to first perform text normalization. In this work, we apply text normalization to noisy English and Dutch text coming from different social media genres: text messages, message board posts and tweets. We consider the normalization task as a Machine Translation problem and test the two leading paradigms: statistical and neural machine translation. For SMT we explore the added value of varying background corpora for training the language model. For NMT we have a look at data augmentation since the parallel datasets we are working with are limited in size. Our results reveal that when relying on SMT to perform the normalization it is beneficial to use a background corpus that is close to the genre you are normalizing. Regarding NMT, we find that the translations - or normalizations - coming out of this model are far from perfect and that for a low-resource language like Dutch adding additional training data works better than artificially augmenting the data.
Anthology ID:
R19-1086
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
740–749
Language:
URL:
https://aclanthology.org/R19-1086
DOI:
10.26615/978-954-452-056-4_086
Bibkey:
Cite (ACL):
Claudia Matos Veliz, Orphee De Clercq, and Veronique Hoste. 2019. Comparing MT Approaches for Text Normalization. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 740–749, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
Comparing MT Approaches for Text Normalization (Matos Veliz et al., RANLP 2019)
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https://preview.aclanthology.org/auto-file-uploads/R19-1086.pdf
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