We present a system that allows users to train their own state-of-the-art paraphrastic sentence representations in a variety of languages. We also release trained models for English, Arabic, German, French, Spanish, Russian, Turkish, and Chinese. We train these models on large amounts of data, achieving significantly improved performance from the original papers proposing the methods on a suite of monolingual semantic similarity, cross-lingual semantic similarity, and bitext mining tasks. Moreover, the resulting models surpass all prior work on unsupervised semantic textual similarity, significantly outperforming even BERT-based models like Sentence-BERT (Reimers and Gurevych, 2019). Additionally, our models are orders of magnitude faster than prior work and can be used on CPU with little difference in inference speed (even improved speed over GPU when using more CPU cores), making these models an attractive choice for users without access to GPUs or for use on embedded devices. Finally, we add significantly increased functionality to the code bases for training paraphrastic sentence models, easing their use for both inference and for training them for any desired language with parallel data. We also include code to automatically download and preprocess training data.

翻译：我们提出一个系统，使用户能够在多种语言中训练自己最先进的释义句子表示。同时，我们发布了英语、阿拉伯语、德语、法语、西班牙语、俄语、土耳其语和中文的预训练模型。这些模型在大规模数据上进行训练，在单语语义相似度、跨语言语义相似度以及双语文本挖掘任务上，相比提出这些方法的原始论文取得了显著性能提升。此外，所得模型在无监督语义文本相似度任务上超越了所有先前工作，甚至显著优于基于BERT的模型（如Sentence-BERT，Reimers与Gurevych，2019）。同时，我们的模型速度比先前工作快数个数量级，可在CPU上使用且推理速度差异极小（使用更多CPU核心时甚至比GPU更快），这使得模型成为无法访问GPU的用户或嵌入式设备应用场景的理想选择。最后，我们显著增强了训练释义句子模型的代码库功能，简化了推理过程以及利用平行数据为任意语言训练模型的操作。我们还包含了自动下载与预处理训练数据的代码。