A Survey of Meaning Representations – From Theory to Practical Utility (2024)

Abstract: Symbolic meaning representations of natural language text have been studied since at least the 1960s. With the availability of large annotated corpora, and more powerful machine learning tools, the field has recently seen several new developments. In this survey, we study today’s most prominent Meaning Representation Frameworks. We shed light on their theoretical properties, as well as on their practical research environment, i.e., on datasets, parsers, applications, and future challenges.

## introduction being able to represent the semantic structure of a text has been an important research goal since the early days of nlp. early works developed natural language interfaces for specific databases. they transformed raw text into an executable language, using formalisms such as sql, first-order logic or lambda-calculus @xcite @xcite . another avenue of research, which is the focus of this work, has developed general-purpose, non-executable meaning representations (mrs), inspired by formal grammars. these often take the form of human-readable graphs. figure 1 shows an example. such mrs are used to improve the accuracy of nlp systems in tasks such as summarization or machine translation @xcite @xcite @xcite . in the age of large language models (llms), they also get leveraged for their interpretability, e.g., to enhance semantic search @xcite @xcite or natural language inference @xcite . they are also used to generate paraphrases @xcite , augment training data @xcite , or to do style-transfer @xcite . in this survey, we provide a structured overview of current meaning representation frameworks. several other surveys have discussed mrs before us. however, they are either focused on linguistic theory @xcite and thus tend to neglect applications, parsers, and resources, or they focus on the practical application only @xcite . our survey aims to strike a middle ground: it presents both the different formalisms and their applications, resources, and parsers. this balance allows us to describe a bigger picture of the field and outline open challenges. our survey thus aims to be a handy reference for anyone who wishes to choose, understand, build, or use a meaning representation. in section 2 we introduce the main concepts and properties of mrs. section 3 tackles shallow mrs, and section 4 deep mrs. finally, section 5 discusses open challenges in the domain. ## meaning representations given a text in natural language, mr parsing is the task of producing a symbolic representation of its meaning, as it is understood by a language speaker @xcite . different meaning representation frameworks (mrfs) have been developed. in this survey, we are interested in graph-like mrfs with their corpora and parsers. we focus on semantic roles (sr), rhetorical structure theory (rst), universal decompositional semantics (uds), semantic dependencies (sd), elementary discourse structures (eds), universal conceptual cognitive annotation (ucca), abstract meaning representation (amr), and discourse representation structure (drs). we will focus on english sentences, and discuss the aspects of multi-sentence and multi-lingual support where relevant. mrfs are often inspired by a neo-davidsonian semantics, and see events as the main elements of sentences. the predicate of an event defines the type of the event, and is most often a verb (decide-01 or fly-01 in figure 1 ). the arguments of the event are the entities that participate in the event ("tiffany" in the example), or the circumstances of the event, such as its place or manner (a negative polarity "-", in our example). the semantic role of an argument specifies the role that the participant plays in the event. in our example, the semantic role of tiffany in the decide-01 event is the subject/agent (arg0 in amr jargon). based on this, the semantic information in a sentence can be decomposed into three levels: the level in the middle describes events. the subevent level decomposes the arguments of events into smaller components -up to words and possibly even sub-words. in our example, "bad for the environment" is modeled by the link from bad-4 to environment with the semantic role arg2. the supra-event level links events to other events. this is done using discourse relations. in our example, the cause-01 node connects decide-01 and bad-04, meaning that the decision was taken because flying is bad for the environment. discourse relations can even link events across sentences. different mrfs vary these general ideas along several axes, which we show in finally, the mrfs differ in their edge type (column 6): some mrfs use roles that depend on a specific linguistic theory, like elaboration (discourse theory) or scene (cognitive science). these schemes can describe only a limited array of relations, and do not distinguish the agents and patients of events. other representations are more specific and use numbered semantic roles (a0, a1, ...). in these schemes, a0 and a1 usually correspond to dowty's proto-agent and proto-patient @xcite , respectively. these proto-roles are defined by their features: typical agent features are awareness, movement, and volition, while typical patient features are change of state, being stationary, etc. the other semantic roles (a2, a3, ...) usually do not have such a predefined meaning. again other mrfs are more specific, and use predicateindependent semantic roles that distinguish finer roles such as agent and patient. finally, some mrfs make the meaning of the role dependent on the predicate: in figure 1 , arg0 means "pilot, agentive entity capable of flight" for fly-01, while it means "decider" for decide-01. these mrfs thus describe their arguments very specifically. ## deep meaning representation frameworks deep meaning representation frameworks go further than shallow ones by representing relations at all levels of the text, in particular at the sub-event level. they aim to model the meaning of the text exhaustively, representing as many phenomena as possible (negations, comparisons, modifiers, time, cause, etc.). ## limitations our survey is limited to graph-like meaning representations. while these are indeed the most popular meaning representations these days, there are others that could be discussed in this survey. raymond mooney's ground-breaking works @xcite @xcite , e.g., or l. zettlemoyer's work on ccg parsing @xcite @xcite , aim at building mrs from a corpus for a target application. the compactness of this survey also prevents us from going more into detail for the parsing techniques. while we do discuss current methods and future trends, parsing itself could merit a survey. the majority of applications presented in section 5 stem from amr. this is simply because amr is the most popular mrf, with very well-performing parsers. however, this does not mean that the other mrfs are less useful. they each have their unique properties that predispose them to different applications.