Predicting the Semantic Textual Similarity with Siamese CNN and LSTM
This work addresses semantic similarity for NLP applications, but it is incremental as it builds on existing neural network approaches.
The paper tackled the problem of measuring semantic textual similarity by combining Siamese CNN and LSTM networks to capture local and global contexts, achieving competitive results with state-of-the-art systems.
Semantic Textual Similarity (STS) is the basis of many applications in Natural Language Processing (NLP). Our system combines convolution and recurrent neural networks to measure the semantic similarity of sentences. It uses a convolution network to take account of the local context of words and an LSTM to consider the global context of sentences. This combination of networks helps to preserve the relevant information of sentences and improves the calculation of the similarity between sentences. Our model has achieved good results and is competitive with the best state-of-the-art systems.