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In this paper, we introduce the task of learning unsupervised dialogueembeddings. Trivial approaches such as combining pre-trained word or sentenceembeddings and encoding through pre-trained language models (PLMs) have beenshown to be feasible for this task. However, these approaches typically ignorethe conversational interactions between interlocutors, resulting in poorperformance. To address this issue, we proposed a self-guided contrastivelearning approach named dial2vec. Dial2vec considers a dialogue as aninformation exchange process. It captures the conversational interactionpatterns between interlocutors and leverages them to guide the learning of theembeddings corresponding to each interlocutor. The dialogue embedding isobtained by an aggregation of the embeddings from all interlocutors. To verifyour approach, we establish a comprehensive benchmark consisting of sixwidely-used dialogue datasets. We consider three evaluation tasks: domaincategorization, semantic relatedness, and dialogue retrieval. Dial2vec achieveson 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 strongestbaseline on the three tasks respectively. Further analysis shows that dial2vecobtains informative and discriminative embeddings for both interlocutors underthe guidance of the conversational interactions and achieves the bestperformance when aggregating them through the interlocutor-level poolingstrategy. All codes and data are publicly available athttps://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/dial2vec.

Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings