Sentence encoders for semantic textual similarity: A survey

The last decade has witnessed many accomplishments in the field of Natural Language Processing, especially in understanding the language semantics. Well-established machine learning models for generating word representation are available and has been proven useful. However, the existing techniques proposed for learning sentence level representations do not adequately capture the complexity of compositional semantics. Finding semantic similarity between sentences is a fundamental language understanding problem. In this project, we compare various machine learning models on their ability to capture the semantics of a sentence using Semantic Textual Similarity (STS) Task. We focus on models that exhibit state-of-the-art performance in Sem-Eval(2017) STS shared task. Also, we analyse the impact of models’ internal architectures on STS task performance. Out of all the models the we compared, Bi-LSTM RNN with max-pooling layer achieves the best performance in extracting a generic semantic representation and aids in better transfer learning when compared to hierarchical CNN.