With the large quantity of textual information produced on the Internet, a critical necessity is to filter out the irrelevant information and organize the rest into categories of interest (e.g., an emerging event). However, supervised-learning document filtering methods heavily rely on a large number of labeled documents for model training. Manually identifying plenty of positive examples for each category is expensive and time-consuming. Also, it is unrealistic to cover all the categories from an evolving text source that covers diverse kinds of events, user opinions, and daily life activities. In this article, we propose a novel attention-based deep relevance model for few-shot document filtering (named ADRM), inspired by the relevance feedback methodology proposed for ad hoc retrieval. ADRM calculates the relevance score between a document and a category by taking a set of seed words and a few seed documents relevant to the category. It constructs the category-specific conceptual representation of the document based on the corresponding seed words and seed documents. Specifically, to filter irrelevant yet noisy information in the seed documents, ADRM employs two types of attention mechanisms (namely whole-match attention and max-match attention ) and generates category-specific representations for them. Then ADRM is devised to extract the relevance signals by modeling the hidden feature interactions in the word embedding space. The relevance signals are extracted through a gated convolutional process, a self-attention layer, and a relevance aggregation layer. Extensive experiments on three real-world datasets show that ADRM consistently outperforms the existing technical alternatives, including the conventional classification and retrieval baselines, and the state-of-the-art deep relevance ranking models for few-shot document filtering. We also perform an ablation study to demonstrate that each component in ADRM is effective for enhancing filtering performance. Further analysis shows that ADRM is robust under varying parameter settings.

中文翻译：

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一种基于注意力的深度相关模型，用于少镜头文档过滤

随着互联网上产生的大量文本信息，一个关键的必要性是过滤掉不相关的信息并将其余信息组织成感兴趣的类别（例如，一个新兴事件）。然而，监督学习文档过滤方法严重依赖大量标记文档进行模型训练。为每个类别手动识别大量正面示例既昂贵又耗时。此外，从涵盖各种事件、用户意见和日常生活活动的不断发展的文本源中涵盖所有类别是不现实的。在本文中，我们提出了一种新颖的基于注意力的深度相关模型，用于小样本文档过滤（命名为 ADRM），其灵感来自为临时检索提出的相关反馈方法。ADRM 通过获取一组种子词和一些与类别相关的种子文档来计算文档和类别之间的相关性分数。它基于相应的种子词和种子文档构造文档的特定类别概念表示。具体来说，为了过滤种子文档中不相关但有噪声的信息，ADRM 采用了两种类型的注意力机制（即全场注意力和最大匹配注意力) 并为它们生成特定于类别的表示。然后设计了 ADRM，通过对词嵌入空间中的隐藏特征交互进行建模来提取相关信号。通过门控卷积过程、自注意力层和相关聚合层提取相关信号。对三个真实世界数据集的大量实验表明，ADRM 始终优于现有的技术替代方案，包括传统的分类和检索基线，以及用于小样本文档过滤的最先进的深度相关性排名模型。我们还进行了消融研究，以证明 ADRM 中的每个组件都可有效提高过滤性能。进一步的分析表明，ADRM 在不同的参数设置下是稳健的。