The transformer has been shown to outperform recurrent neural network-based sequence-to-
sequence models in various word-level NLP tasks. Yet for character-level transduction tasks …

There is no consensus on the state-of-the-art approach to historical text normalization. Many
techniques have been proposed, including rule-based methods, distance metrics, character …

Development sets are impractical to obtain for real low-resource languages, since using all
available data for training is often more effective. However, development sets are widely …

In this paper, we apply different NMT models to the problem of historical spelling
normalization for five languages: English, German, Hungarian, Icelandic, and Swedish. The …

The paper presents the results of the Janes project, which aimed to develop language
resources and tools for Slovene user generated content. The paper first describes the 200 …

Spelling normalisation is a useful step in the study and analysis of historical language texts,
whether it is manual analysis by experts or automatic analysis using downstream natural …

Automated processing of historical texts often relies on pre-normalization to modern word
forms. Training encoder-decoder architectures to solve such problems typically requires a lot …

With the increasing availability of digitized resources of historical documents, interest in
effective natural language processing (NLP) for these documents is on the rise. However …

Abstract The Swiss German dialect corpus Archi-Mob poses great challenges for NLP and
corpus linguistic research due to the massive amount of variation found in the transcriptions …

In this paper, we introduce and demonstrate the online demo as well as the command line
interface of a lexical normalization system (MoNoise) for a variety of languages. We further …