  Lexical normalization, a fundamental task in Natural Language Processing
(NLP), involves the transformation of words into their canonical forms. This
process has been proven to benefit various downstream NLP tasks greatly. In
this work, we introduce Vietnamese Lexical Normalization (ViLexNorm), the
first-ever corpus developed for the Vietnamese lexical normalization task. The
corpus comprises over 10,000 pairs of sentences meticulously annotated by human
annotators, sourced from public comments on Vietnam's most popular social media
platforms. Various methods were used to evaluate our corpus, and the
best-performing system achieved a result of 57.74% using the Error Reduction
Rate (ERR) metric (van der Goot, 2019a) with the Leave-As-Is (LAI) baseline.
For extrinsic evaluation, employing the model trained on ViLexNorm demonstrates
the positive impact of the Vietnamese lexical normalization task on other NLP
tasks. Our corpus is publicly available exclusively for research purposes.
