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.

本文提出了一个用于研究复杂比喻现象的多语言比喻对话数据集（MSD），其中包含三个比喻任务（识别，解释和生成）和两个对话任务（检索和生成）的实验结果，并公开数据和代码以便更深入研究。

多语言比喻对话数据集：我像兔子一样快，你呢？

I run as fast as a rabbit, can you? A Multilingual Simile Dialogue  Dataset

We consider the use of Deep Learning methods for modeling complex phenomena
like those occurring in natural physical processes. With the large amount of
data gathered on these phenomena the data intensive paradigm could begin to
challenge more traditional approaches elaborated over the years in fields like
maths or physics. However, despite considerable successes in a variety of
application domains, the machine learning field is not yet ready to handle the
level of complexity required by such problems. Using an example application,
namely Sea Surface Temperature Prediction, we show how general background
knowledge gained from physics could be used as a guideline for designing
efficient Deep Learning models. In order to motivate the approach and to assess
its generality we demonstrate a formal link between the solution of a class of
differential equations underlying a large family of physical phenomena and the
proposed model. Experiments and comparison with series of baselines including a
state of the art numerical approach is then provided.

用深度学习方法建模复杂现象，以海表温度预测为例，展示了从物理学中获取的背景知识如何指导设计高效的深度学习模型，并证明了一个物理现象的微分方程的解与提出的模型之间的形式上的联系。