Product catalogs, conceptually in the form of
text-rich tables, are self-reported by individual
retailers and thus inevitably contain noisy
facts. Verifying such textual attributes in product
catalogs is essential to improve their reliability.
However, popular methods for processing
free-text content, such as pre-trained
language models, are not particularly effective
on structured tabular data since they are typically
trained on free-form natural language
texts. In this paper, we present Tab-Cleaner, a
model designed to handle error detection over
text-rich tabular data following a pre-training
/ fine-tuning paradigm. We train Tab-Cleaner
on a real-world Amazon Product Catalog table
w.r.t millions of products and show improvements
over state-of-the-art methods by 16% on
PR AUC over attribute applicability classification
task and by 11% on PR AUC over attribute
value validation task.
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Free, publicly-accessible full text available July 1, 2024
