论文标题
通用句子表示的上下文镜头
Contextual Lensing of Universal Sentence Representations
论文作者
论文摘要
是什么使通用句子编码器通用？文本的通用编码器的概念似乎与动态世界中语言使用的固有上下文化和非永久性相矛盾。但是，将句子映射到通用的固定长度向量中，以进行下游相似性和检索任务，尤其是对于多语言应用而言。我们如何解决这个困境？在这项工作中，我们提出了上下文镜头，这是一种诱导面向上下文的通用句子向量的方法。我们将通用句子向量的构造分解为核心，可变长度，句子矩阵表示，配备了适应性的“镜头”，可以从中从中诱导固定长度向量作为镜头上下文的函数。我们表明，在给定核心通用矩阵表示的情况下，可以将语言相似性的概念集中在少数镜头参数中。例如，我们证明了将句子跨多种语言编码句子相似性的能力，即使核心编码器未看到并行数据，也可以将句子的翻译相似。
What makes a universal sentence encoder universal? The notion of a generic encoder of text appears to be at odds with the inherent contextualization and non-permanence of language use in a dynamic world. However, mapping sentences into generic fixed-length vectors for downstream similarity and retrieval tasks has been fruitful, particularly for multilingual applications. How do we manage this dilemma? In this work we propose Contextual Lensing, a methodology for inducing context-oriented universal sentence vectors. We break the construction of universal sentence vectors into a core, variable length, sentence matrix representation equipped with an adaptable `lens' from which fixed-length vectors can be induced as a function of the lens context. We show that it is possible to focus notions of language similarity into a small number of lens parameters given a core universal matrix representation. For example, we demonstrate the ability to encode translation similarity of sentences across several languages into a single weight matrix, even when the core encoder has not seen parallel data.
