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There have been many successful applications of sentence embedding methods.However, it has not been well understood what properties are captured in theresulting sentence embeddings depending on the supervision signals. In thispaper, we focus on two types of sentence embedding methods with similararchitectures and tasks: one fine-tunes pre-trained language models on thenatural language inference task, and the other fine-tunes pre-trained languagemodels on word prediction task from its definition sentence, and investigatetheir properties. Specifically, we compare their performances on semantictextual similarity (STS) tasks using STS datasets partitioned from twoperspectives: 1) sentence source and 2) superficial similarity of the sentencepairs, and compare their performances on the downstream and probing tasks.Furthermore, we attempt to combine the two methods and demonstrate thatcombining the two methods yields substantially better performance than therespective methods on unsupervised STS tasks and downstream tasks.

Comparison and Combination of Sentence Embeddings Derived from Different Supervision Signals