The paraphrase identification task involves measuring semantic similarity
between two short sentences. It is a tricky task, and multilingual paraphrase
identification is even more challenging. In this work, we train a bi-encoder
model in a contrastive manner to detect hard paraphrases across multiple
languages. This approach allows us to use model-produced embeddings for various
tasks, such as semantic search. We evaluate our model on downstream tasks and
also assess embedding space quality. Our performance is comparable to
state-of-the-art cross-encoders, with only a minimal relative drop of 7-10% on
the chosen dataset, while keeping decent quality of embeddings.

通过对比式编码器模型在多语言环境下进行训练，实现检测多语言环境中的困难释义，得到可用于语义搜索等多种任务的嵌入向量，并在下游任务中评估模型性能和嵌入向量质量，与当前最先进的交叉编码器相比，在所选择的数据集中只有 7-10% 的最小相对下降，同时保持嵌入向量的良好质量。

跨语言释义识别

Cross-lingual paraphrase identification

Graphs are a representation of structured data that captures the
relationships between sets of objects. With the ubiquity of available network
data, there is increasing industrial and academic need to quickly analyze
graphs with billions of nodes and trillions of edges. A common first step for
network understanding is Graph Embedding, the process of creating a continuous
representation of nodes in a graph. A continuous representation is often more
amenable, especially at scale, for solving downstream machine learning tasks
such as classification, link prediction, and clustering. A high-performance
graph embedding architecture leveraging Tensor Processing Units (TPUs) with
configurable amounts of high-bandwidth memory is presented that simplifies the
graph embedding problem and can scale to graphs with billions of nodes and
trillions of edges. We verify the embedding space quality on real and synthetic
large-scale datasets.

图嵌入是网络理解的常用方法，本论文提出了一种基于 Tensor Processing Units 的高性能图嵌入架构，能够解决大规模图的嵌入问题，并通过真实和合成的大规模数据集验证了嵌入空间的质量。