We present a novel approach to learn representations for sentence-level
semantic similarity using conversational data. Our method trains an
unsupervised model to predict conversational input-response pairs...
resulting sentence embeddings perform well on the semantic textual similarity
(STS) benchmark and SemEval 2017's Community Question Answering (CQA) question
similarity subtask. Performance is further improved by introducing multitask
training combining the conversational input-response prediction task and a
natural language inference task. Extensive experiments show the proposed model
achieves the best performance among all neural models on the STS benchmark and
is competitive with the state-of-the-art feature engineered and mixed systems
in both tasks.