A Survey of AMR Applications (2024)

Abstract: In the ten years since the development of the Abstract Meaning Representation (AMR) formalism, substantial progress has been made on AMR-related tasks such as parsing and alignment. Still, the engineering applications of AMR are not fully understood. In this survey, we categorize and characterize more than 100 papers which use AMR for downstream tasks— the first survey of this kind for AMR. Specifically, we highlight (1) the range of applications for which AMR has been harnessed, and (2) the techniques for incorporating AMR into those applications. We also detect broader AMR engineering patterns and outline areas of future work that seem ripe for AMR incorporation. We hope that this survey will be useful to those interested in using AMR and that it sparks discussion on the role of symbolic representations in the age of neural-focused NLP research.

## introduction abstract meaning representation (amr; @xcite is a semantic representation that takes the form of a rooted, directed graph. since the release of amr in 2013, a full amrecosystem has emerged, with substantial research activity on amr annotation, text-to-amr parsing, amr-to-text generation, and domain-and language-based extensions of amr. 1 in particular, the progress on text-to-amr parsing and amrto-text generation has propelled work using amr for various nlp applications. to date, downstream applications of amr have been spread across numerous tasks and have found varying degrees of success. thus, given the recent advancements for and with amr, this survey addresses the pressing question: how can amr be used for engineering purposes and downstream applications? our main figure 1 : the amr for the sentence "after 3 days and much deliberation, the jury rendered a guilty verdict," as a graph (top) and as a string in penman notation (bottom). our investigation is also motivated by the prevalence of large language models (llms) that seem to be able to generalize across a large suite of nlp tasks, prompting consideration of how semantic representations can remain useful. we hope that our survey will serve both as a useful starting point and a source of inspiration to those interested in working with amr. ## applications of amr in this section, we will discuss the broad categories of downstream uses of amr in natural language processing. we present more than 20 individual areas of application, and discuss how each work incorporated amr. ## engineering with amr in the prior section, we categorized amr applications by task in order to provide an overview of the amr application landscape, showcasing amr as a general-purpose representation. in this section, we describe and provide a functional guide to the techniques and patterns which have allowed amr to be leveraged for the aforementioned engineering purposes. ## areas for future work surveying the vast number of tasks and techniques utilized throughout the last decade, we observe three notable areas for future work on amr applications. first, a technique which has shown great promise for incorporating amr into neural or non-neural downstream applications is as an intermediate representation. this intuitively leverages both amr's design as a graph-based semantic representation as well as the progress on text-to-amr parsing and amr-to-text generation. 4 using amr as an intermediary provides us linguistic control and interpretability, which are increasingly desirable in the age of "black box" neural models. numerous recent studies have successfully exploited amr as an intermediary ( §4.2), indicating that this may be a promising path forward, particularly in lowresource settings or for data augmentation. second, recent work has shown the benefits of incorporating amr in few-shot or low-resource settings @xcite @xcite . this indicates that, regardless of the technique of incorporation, amr is positioned to be especially well suited for engineering gains in these settings. finally, an area which has been largely understudied ( §3.6) but directly follows from the design of amr as semantic representation, is the use of amr for linguistic analysis and text statistics. potential applications include language learning or studying predicate-argument patterns in l1 or l2 texts. additional uses of amr for linguistic analysis include linguistically-focused evaluations and finer statistics of nlg systems (prior work in this direction discussed in §3.5). 5 ## related work on applications of other meaning representations other surveys have considered the engineering utility of semantic representations. regarding specific tasks, verrev (2023) tested out the benefits of various meaning representations for knowledgebase question answering (kbqa), and @xcite for next-word prediction in conjunction with neural models. related work has also compared the designs and features of semantic representations @xcite , with @xcite specif-ically addressing how the features of the semantic representations may play a role in their utility. ## conclusion in this survey, we provided a thorough overview of the tasks where abstract meaning representation graphs have been used and the techniques involved in using amr for engineering purposes. given the availability of strong parsing systems and the increased interest in amr, we expect that we are on the precipice of exciting progress employing amr in downstream applications. as our synthesis of amr engineering patterns indicates, there are numerous methods, techniques and possible applications that await further exploration and continued improvement.