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.

本研究利用 RoBERTa 和 XLM-R 语言模型，对在线产品评论的帮助度进行有效预测。研究表明，相较于传统特征工程，使用预训练语言模型能达到更好的预测效果，并且多语言模型在某些情况下表现更佳。但是，多语言模型并不能用于单一语言的微调。同时，评论人打的分数等附加特征也可以增强模型的预测准确度。

评估预训练语言模型在预测在线产品评论的帮助性方面的有效性

Evaluating the Effectiveness of Pre-trained Language Models in Predicting the Helpfulness of Online Product Reviews

Product-specific community question answering platforms can greatly help
address the concerns of potential customers. However, the user-provided answers
on such platforms often vary a lot in their qualities. Helpfulness votes from
the community can indicate the overall quality of the answer, but they are
often missing. Accurately predicting the helpfulness of an answer to a given
question and thus identifying helpful answers is becoming a demanding need.
Since the helpfulness of an answer depends on multiple perspectives instead of
only topical relevance investigated in typical QA tasks, common answer
selection algorithms are insufficient for tackling this task. In this paper, we
propose the Review-guided Answer Helpfulness Prediction (RAHP) model that not
only considers the interactions between QA pairs but also investigates the
opinion coherence between the answer and crowds' opinions reflected in the
reviews, which is another important factor to identify helpful answers.
Moreover, we tackle the task of determining opinion coherence as a language
inference problem and explore the utilization of pre-training strategy to
transfer the textual inference knowledge obtained from a specifically designed
trained network. Extensive experiments conducted on real-world data across
seven product categories show that our proposed model achieves superior
performance on the prediction task.

本文提出了一种基于评论引导的答案有用性预测模型，该模型不仅考虑了 QA 对之间的交互，还探讨了答案与众筹意见之间的意见连贯性作为识别有用答案的另一个重要因素，并探索了利用预训练策略将从一个特定设计的经过训练的网络中获得的文本推理知识转移的方法。该模型在七个产品类别上进行的广泛实验表明，其在预测任务中的表现优异。