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In Semantic Textual Similarity, systems rate the degree of semantic equivalence on a graded scale from 0 to 5, with 5 being the most similar. For the English subtask, we present a system which relies on several resources for token-to-token and phrase-to-phrase similarity to build a data-structure which holds all the information, and then combine the information to get a similarity score. We also participated in the pilot on Interpretable STS, where we apply a pipeline which first aligns tokens, then chunks, and finally uses supervised systems to label and score each chunk alignment.

UBC: Cubes for English Semantic Textual Similarity and Supervised Approaches for Interpretable STS