ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity

A growing body of research has recently been conducted on semantic textual similarity using a variety of neural network models. While recent research focuses on word-based representation for phrases, sentences and even paragraphs, this study considers an alternative approach based on character n-grams. We generate embeddings for character n-grams using a continuous-bag-of-n-grams neural network model. Three different sentence representations based on n-gram embeddings are considered. Results are reported for experiments with bigram, trigram and 4-gram embeddings on the STS Core dataset for SemEval-2016 Task 1.