Online customer reviews on large-scale e-commerce websites, represent a rich and varied source of opinion data, often providing subjective qualitative assessments of product usage that can help potential customers to discover features that meet their personal needs and preferences. Thus they have the potential to automatically answer specific queries about products, and to address the problems of answer starvation and answer augmentation on associated consumer Q & A forums, by providing good answer alternatives. In this work, we explore several recently successful neural approaches to modeling sentence pairs, that could better learn the relationship between questions and ground truth answers, and thus help infer reviews that can best answer a question or augment a given answer. In particular, we hypothesize that our neural domain adaptation-based approach, due to its ability to additionally learn domain-invariant features from a large number of unlabeled, unpaired question-review samples, would perform better than our proposed baselines, at answering specific, subjective product-related queries using reviews. We validate this hypothesis using a small gold standard dataset of question-review pairs evaluated by human experts, significantly surpassing our chosen baselines. Moreover, our approach, using no labeled question-review sentence pair data for training, gives performance at par with another method utilizing labeled question-review samples for the same task.
翻译:在大型电子商务网站上,在线客户审查是一个丰富多样的意见数据来源,对产品使用情况进行主观的定性评估,帮助潜在客户发现满足个人需要和喜好的特点,从而能够自动回答关于产品的具体问题,解决答案饥饿问题,解决相关消费者问答论坛增加的问题,提供良好的答案选项。在这项工作中,我们探索了最近成功的若干个神经学方法来模拟对等判决,这可以更好地了解问题与地面真相答案之间的关系,从而帮助推断出对产品使用情况的审查,从而能够最好地回答一个问题或增加给定答案。特别是,我们假设我们基于神经领域的适应方法,因为它能够从大量未标注、未标注的问答审查样本中更多地学习域差异特征,因此有可能解决相关消费者问答论坛中的问题。在用审查来回答具体的、主观的与产品有关的问题查询时,比我们提议的基线更好。我们使用由人类专家评估的小型金质标准对组合数据来验证这一假设,大大超过我们选定的基线。此外,我们假设我们的神经领域适应方法,因为我们的神经领域适应方法是因为它能够更多地学习大量学习无名无名、无名的域审查数据,在使用同一的标签上对任务进行同样的审查的抽样审查。