Before revision:  In this work, we present a novel framework on BERT that is capable of generating universal, fixed-size representations for input sequences of any lengths , i.e., words, phrases, and sentences, using a large scale of natural language inference and paraphrase data with multiple training objectives.
After revision:  In this work, we present BURT (BERT inspired Universal Representation from Twin Structure) that is capable of generating universal, fixed-size representations for input sequences of any lengths , i.e., words, phrases, and sentences, using a large scale of natural language inference and paraphrase data with multiple training objectives.