Despite enormous progress in Natural Language Processing (NLP), our field is still lacking a common deep semantic representation scheme. As a result, the problem of meaning and understanding is typically sidestepped through more simple, approximative methods. This paper argues that in order to arrive at such a scheme, we also need a common modelling scheme. It therefore introduces MetaSRL++, a uniform, language- and modality-independent modelling scheme based on Semantic Graphs, as a step towards a common representation scheme; as well as a method for defining the concepts and entities that are used in these graphs. Our output is twofold. First, we illustrate MetaSRL++ through concrete examples. Secondly, we discuss how it relates to existing work in the field.

翻译：尽管自然语言处理（NLP）取得了巨大进展，该领域仍缺乏一种通用的深层语义表示方案。因此，意义与理解问题通常通过更简单、近似的途径被规避。本文认为，要达成这样的方案，我们还需要一种通用建模方案。为此，本文提出MetaSRL++——一种基于语义图的统一、语言无关且模态无关的建模方案——作为迈向通用表示方案的一步；同时提出一种定义这些图中所用概念与实体的方法。我们的输出包含两方面：首先，通过具体示例阐释MetaSRL++；其次，讨论其与现有领域工作的关联。