  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. Dial2vec considers a dialogue as an
information exchange process. It captures the conversational interaction
patterns between interlocutors and leverages them to guide the learning of the
embeddings corresponding to each interlocutor. The dialogue embedding is
obtained by an aggregation of the embeddings from all interlocutors. To verify
our approach, we establish a comprehensive benchmark consisting of six
widely-used dialogue datasets. We consider three evaluation tasks: domain
categorization, semantic relatedness, and dialogue retrieval. Dial2vec achieves
on average 8.7, 9.0, and 13.8 points absolute improvements in terms of purity,
Spearman's correlation, and mean average precision (MAP) over the strongest
baseline on the three tasks respectively. Further analysis shows that dial2vec
obtains informative and discriminative embeddings for both interlocutors under
the guidance of the conversational interactions and achieves the best
performance when aggregating them through the interlocutor-level pooling
strategy. All codes and data are publicly available at
https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/dial2vec.
