Learning high quality sentence embeddings from dialogues has drawn increasing attentions as it is essential to solve a variety of dialogue-oriented tasks with low annotation cost. However, directly annotating and gathering utterance relationships in conversations are difficult, while token-level annotations, \eg, entities, slots and templates, are much easier to obtain. General sentence embedding methods are usually sentence-level self-supervised frameworks and cannot utilize token-level extra knowledge. In this paper, we introduce Template-aware Dialogue Sentence Embedding (TaDSE), a novel augmentation method that utilizes template information to effectively learn utterance representation via self-supervised contrastive learning framework. TaDSE augments each sentence with its corresponding template and then conducts pairwise contrastive learning over both sentence and template. We further enhance the effect with a synthetically augmented dataset that enhances utterance-template relation, in which entity detection (slot-filling) is a preliminary step. We evaluate TaDSE performance on five downstream benchmark datasets. The experiment results show that TaDSE achieves significant improvements over previous SOTA methods, along with a consistent Intent Classification task performance improvement margin. We further introduce a novel analytic instrument of Semantic Compression method, for which we discover a correlation with uniformity and alignment. Our code will be released soon.

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