Attentive Siamese LSTM Network for Semantic Textual Similarity Measure

Semantic Textual Similarity (STS) is important for many applications such as Plagiarism Detection (PD), Text Paraphrasing and Information Retrieval (IR). Current methods for STS rely on statistical machine learning. Recent studies showed that neural networks for STS presented promising experimental results. In this paper, we propose an Attentive Siamese Long Short-Term Memory (LSTM) network for measuring Semantic Textual Similarity. Instead of external resources and handcraft features, raw sentence pairs and pre-trained word embedding are needed as input. Attention mechanism is utilized in LSTM network to capture high-level semantic information. We demonstrated the effectiveness of our model by applying the architecture in different tasks: three corpora and three language tasks. Experimental results on all tasks and languages show that our method with attention mechanism outperforms the baseline model with a higher correlation with human annotation.