  A simile is a figure of speech that compares two different things (called the
tenor and the vehicle) via shared properties. The tenor and the vehicle are
usually connected with comparator words such as "like" or "as". The simile
phenomena are unique and complex in a real-life dialogue scene where the tenor
and the vehicle can be verbal phrases or sentences, mentioned by different
speakers, exist in different sentences, or occur in reversed order. However,
the current simile research usually focuses on similes in a triplet tuple
(tenor, property, vehicle) or a single sentence where the tenor and vehicle are
usually entities or noun phrases, which could not reflect complex simile
phenomena in real scenarios. In this paper, we propose a novel and high-quality
multilingual simile dialogue (MSD) dataset to facilitate the study of complex
simile phenomena. The MSD is the largest manually annotated simile data
($\sim$20K) and it contains both English and Chinese data. Meanwhile, the MSD
data can also be used on dialogue tasks to test the ability of dialogue systems
when using similes. We design 3 simile tasks (recognition, interpretation, and
generation) and 2 dialogue tasks (retrieval and generation) with MSD. For each
task, we provide experimental results from strong pre-trained or
state-of-the-art models. The experiments demonstrate the challenge of MSD and
we have released the data/code on GitHub.
