The rapid development of Internet of Things (IoT) brings an urgent requirement on intelligent human–device interactions using natural language, which are critical for facilitating people to use IoT devices. The efficient interactive approaches depend on various natural language understanding technologies. Among them, sentence pair modeling (SPM) is essential, where neural networks have achieved great success in SPM area due to their powerful abilities in feature extraction and representation. However, as sentences are one-dimensional (1D) texts, the available neural networks are usually limited to 1D sequential models, which prevents the performance improvement of SPM task. To address this gap, in this paper, we propose a novel neural architecture for sentence pair modeling, which utilizes 1D sentences to construct multi-dimensional feature maps similar to images containing multiple color channels. Based on the feature maps, more kinds of neural models become applicable on SPM task, including 2D CNN. In the proposed model, first, the sentence on a specific granularity is encoded with BiLSTM to generate the representation on this granularity, which is viewed as a special channel of the sentence. The representations from different granularity are merged together to construct semantic feature map of the input sentence. Then, 2D CNN is employed to encode the feature map to capture the deeper semantic features contained in the sentence. Next, another 2D CNN is utilized to capture the interactive matching features between sentences, followed by 2D max-pooling and attention mechanism to generate the final matching representation. Finally, the matching degree of sentences are judged with a sigmoid function according to the matching representation. Extensive experiments are conducted on two real-world data sets. In comparison with benchmarks, the proposed model achieved remarkable results, and performed better or comparably with BERT-based models. Our work is beneficial to building a more powerful humanized interaction system with IoT devices.

物联网（IoT）的快速发展对使用自然语言的智能人机交互提出了迫切的需求，这对于促进人们使用物联网设备至关重要。有效的交互方法取决于各种自然语言理解技术。其中，句子对建模（SPM）是必不可少的，神经网络由于其强大的特征提取和表示能力，在SPM领域取得了巨大的成功。然而，由于句子是一维 (1D) 文本，可用的神经网络通常仅限于一维序列模型，这阻碍了 SPM 任务的性能提升。为了解决这个差距，在本文中，我们提出了一种用于句子对建模的新型神经架构，它利用一维句子来构建类似于包含多个颜色通道的图像的多维特征图。基于特征图，更多种类的神经模型适用于 SPM 任务，包括 2D CNN。在所提出的模型中，首先，特定粒度上的句子用 BiLSTM 编码以生成该粒度上的表示，这被视为句子的特殊通道。将来自不同粒度的表示合并在一起以构建输入句子的语义特征图。然后，采用 2D CNN 对特征图进行编码，以捕获句子中包含的更深层次的语义特征。接下来，利用另一个 2D CNN 来捕获句子之间的交互匹配特征，然后是 2D 最大池化和注意力机制以生成最终的匹配表示。最后，根据匹配表示，用sigmoid函数判断句子的匹配程度。在两个真实世界的数据集上进行了广泛的实验。与基准相比，所提出的模型取得了显着的结果，并且与基于 BERT 的模型表现更好或相当。我们的工作有利于构建更强大的物联网设备人性化交互系统。