  Businesses and customers can gain valuable information from product reviews.
The sheer number of reviews often necessitates ranking them based on their
potential helpfulness. However, only a few reviews ever receive any helpfulness
votes on online marketplaces. Sorting all reviews based on the few existing
votes can cause helpful reviews to go unnoticed because of the limited
attention span of readers. The problem of review helpfulness prediction is even
more important for higher review volumes, and newly written reviews or launched
products. In this work we compare the use of RoBERTa and XLM-R language models
to predict the helpfulness of online product reviews. The contributions of our
work in relation to literature include extensively investigating the efficacy
of state-of-the-art language models -- both monolingual and multilingual --
against a robust baseline, taking ranking metrics into account when assessing
these approaches, and assessing multilingual models for the first time. We
employ the Amazon review dataset for our experiments. According to our study on
several product categories, multilingual and monolingual pre-trained language
models outperform the baseline that utilizes random forest with handcrafted
features as much as 23% in RMSE. Pre-trained language models reduce the need
for complex text feature engineering. However, our results suggest that
pre-trained multilingual models may not be used for fine-tuning only one
language. We assess the performance of language models with and without
additional features. Our results show that including additional features like
product rating by the reviewer can further help the predictive methods.
