# Adapting Sequence to Sequence models for Text Normalization in Social Media

Source: [Adapting Sequence to Sequence models for Text Normalization in Social Media](<https://www.sota2.com/research/paper/68fa659e1e209131cea0b352>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-25T07:31:35.193Z

Authors: Ismini Lourentzou, Kabir Manghnani, ChengXiang Zhai

Published: 2019-04-12T08:45:43.000Z

## Abstract

Social media offer an abundant source of valuable raw data, however informal writing can quickly become a bottleneck for many natural language processing (NLP) tasks. Off-the-shelf tools are usually trained on formal text and cannot explicitly handle noise found in short online posts. Moreover, the variety of frequently occurring linguistic variations presents several challenges, even for humans who might not be able to comprehend the meaning of such posts, especially when they contain slang and abbreviations. Text Normalization aims to transform online user-generated text to a canonical form. Current text normalization systems rely on string or phonetic similarity and classification models that work on a local fashion. We argue that processing contextual information is crucial for this task and introduce a social media text normalization hybrid word-character attention-based encoder-decoder model that can serve as a pre-processing step for NLP applications to adapt to noisy text in social media. Our character-based component is trained on synthetic adversarial examples that are designed to capture errors commonly found in online user-generated text. Experiments show that our model surpasses neural architectures designed for text normalization and achieves comparable performance with state-of-the-art related work.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Text Normalization on LexNorm 2015 (test)](<https://www.sota2.com/research/sota/text-normalization-on-lexnorm-2015-test.md>): Text Normalization; LexNorm 2015 (test); Precision: 90.66.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1904.06100>)

[PDF](<https://arxiv.org/pdf/1904.06100.pdf>)

[Implementation](<https://github.com/Isminoula/TextNormSeq2Seq>)