Semantic text similarity (STS) is a challenging issue for natural language processing due to linguistic expression variability and ambiguities. The degree of the likelihood between the two sentences is calculated by sentence similarity. It plays a prominent role in many applications like information retrieval (IR), plagiarism detection (PD), question answering platform and text paraphrasing, etc. Now, deep contextualised word representations became a better way for feature extraction in sentences. It has shown exciting experimental results from recent studies. In this paper, we propose a deep contextual long semantic textual similarity network. Deep contextual mechanisms for collecting high-level semantic knowledge is used in the LSTM network. Through implementing architecture in multiple datasets, we have demonstrated our model’s effectiveness. By applying architecture to various semantic similarity datasets, we showed the usefulness of our model’s on regression and classification dataset. Detailed experimentation and results show that the proposed deep contextual model performs better than the human annotation.

由于语言表达的可变性和歧义，语义文本相似性 (STS) 是自然语言处理的一个具有挑战性的问题。通过句子相似度计算两个句子之间的似然程度。它在信息检索（IR）、抄袭检测（PD）、问答平台和文本释义等许多应用中发挥着突出的作用。现在，深度上下文化的词表示成为句子特征提取的更好方法。它显示了最近研究中令人兴奋的实验结果。在本文中，我们提出了一个深度上下文长语义文本相似性网络。LSTM 网络中使用了用于收集高级语义知识的深层上下文机制。通过在多个数据集中实现架构，我们已经证明了我们模型的有效性。通过将架构应用于各种语义相似性数据集，我们展示了我们的模型在回归和分类数据集上的有用性。详细的实验和结果表明，所提出的深度上下文模型的性能优于人工注释。