Target-oriented opinion words extraction (TOWE) seeks to identify opinion expressions oriented to a specific target, and it is a crucial step toward fine-grained opinion mining. Recent neural networks have achieved significant success in this task by building target-aware representations. However, there are still two limitations of these methods that hinder the progress of TOWE. Mainstream approaches typically utilize position indicators to mark the given target, which is a naive strategy and lacks task-specific semantic meaning. Meanwhile, the annotated target-opinion pairs contain rich latent structural knowledge from multiple perspectives, but existing methods only exploit the TOWE view. To tackle these issues, we formulate the TOWE task as a question answering (QA) problem and leverage a machine reading comprehension (MRC) model trained with a multiview paradigm to extract targeted opinions. Specifically, we introduce a template-based pseudo-question generation method and utilize deep attention interaction to build target-aware context representations and extract related opinion words. To take advantage of latent structural correlations, we further cast the opinion-target structure into three distinct yet correlated views and leverage meta-learning to aggregate common knowledge among them to enhance the TOWE task. We evaluate the proposed model on four benchmark datasets, and our method achieves new state-of-the-art results. Extensional experiments have shown that the pipeline method with our approach could surpass existing opinion pair extraction models, including joint methods that are usually believed to work better.

中文翻译：

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多视图训练的机器阅读理解模型对面向目标的舆论词提取的增强

面向目标的意见词提取（TOWE）旨在识别针对特定目标的意见表达，这是迈向细粒度的意见挖掘的关键一步。通过构建目标感知表示，最近的神经网络已在此任务中取得了重大成功。但是，这些方法仍然存在两个局限性，阻碍了TOWE的发展。主流方法通常利用位置指示器来标记给定的目标，这是一种幼稚的策略，缺乏特定于任务的语义。同时，带注释的目标意见对从多个角度包含丰富的潜在结构知识，但是现有方法仅利用TOWE视图。为了解决这些问题，我们将TOWE任务表述为问答（QA）问题，并利用经过多视图范例训练的机器阅读理解（MRC）模型来提取目标意见。具体来说，我们介绍了一种基于模板的伪问题生成方法，并利用深度关注交互来构建目标感知的上下文表示并提取相关的意见词。为了利用潜在的结构相关性，我们将意见-目标结构进一步转换为三个截然不同但相关的视图，并利用元学习来汇总它们之间的常识以增强TOWE任务。我们在四个基准数据集上评估了提出的模型，并且我们的方法获得了最新的结果。