Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings

Understand

In this paper, we introduce the task of learning unsupervised dialogue embeddings.

• Trivial approaches such as combining pre-trained word or sentence embeddings and encoding through pre-trained language models (PLMs) have been shown to be feasible for this task.
• However, these approaches typically ignore the conversational interactions between interlocutors, resulting in poor performance.
• To address this issue, we proposed a self-guided contrastive learning approach named dial2vec.