MoNoise: Modeling Noise Using a Modular Normalization System

We propose MoNoise: a normalization model focused on generalizability and efficiency, it aims at being easily reusable and adaptable. Normalization is the task of translating texts from a non- canonical domain to a more canonical domain, in our case: from social media data to standard language. Our proposed model is based on a modular candidate generation in which each module is responsible for a different type of normalization action. The most important generation modules are a spelling correction system and a word embeddings module. Depending on the definition of the normalization task, a static lookup list can be crucial for performance. We train a random forest classifier to rank the candidates, which generalizes well to all different types of normaliza- tion actions. Most features for the ranking originate from the generation modules; besides these features, N-gram features prove to be an important source of information. We show that MoNoise beats the state-of-the-art on different normalization benchmarks for English and Dutch, which all define the task of normalization slightly different.Comment: Source code: https://bitbucket.org/robvanderg/monois

Enhancing BERT for Lexical Normalization

International audienceLanguage model-based pre-trained representations have become ubiquitous in natural language processing. They have been shown to significantly improve the performance of neu-ral models on a great variety of tasks. However , it remains unclear how useful those general models can be in handling non-canonical text. In this article, focusing on User Generated Content (UGC) in a resource-scarce scenario , we study the ability of BERT (Devlin et al., 2018) to perform lexical normalisation. Our contribution is simple: by framing lexical normalisation as a token prediction task, by enhancing its architecture and by carefully fine-tuning it, we show that BERT can be a competitive lexical normalisation model without the need of any UGC resources aside from 3,000 training sentences. To the best of our knowledge , it is the first work done in adapting and analysing the ability of this model to handle noisy UGC data