SPRING G oes O nline: E nd-to- E nd AMR P arsing and G eneration (2021)

Abstract: In this paper we present SPRING Online Services, a Web interface and RESTful APIs for our state-of-the-art AMR parsing and generation system, SPRING (Symmetric PaRsIng aNd Generation). The Web interface has been developed to be easily used by the Natural Language Processing community, as well as by the general public. It provides, among other things, a highly interactive visualization platform and a feedback mechanism to obtain user suggestions for further improvements of the system’s output. Moreover, our RESTful APIs enable easy integration of SPRING in downstream applications where AMR structures are needed. Finally, we make SPRING Online Services freely available at http://nlp.uniroma1.it/spring and, in addition, we release extra model checkpoints to be used with the original SPRING Python code.

## introduction abstract meaning representation @xcite is a popular formalism for representing the semantics of natural language in a readable and hierarchical way. amr pairs english sentences with graph-based logical formulas which are easily accessible by both humans and machines, while abstracting away from many syntactic variations. because of the formalism's ambition to be comprehensive, amr graphs are complex objects that require a parser -an automatic algorithm that transduces a natural language utterance into an amr graph -to subsume multiple traditional natural language processing tasks: word sense disambiguation @xcite , semantic role labeling @xcite @xcite , named entity recognition @xcite , entity linking @xcite , and coreference resolution @xcite . owing to this complexity, amr parsing, as well as its specular counterpart, i.e., amr generation, are hard to solve. however, the richness of the information included in amr graphs, as well as their obvious applications as an interface between human and machines, make both amr parsing and generation very rewarding problems to solve. as a matter of fact, amr has been successfully applied to diverse downstream applications, such as machine translation @xcite , text summarization @xcite , human-robot interaction @xcite , information extraction @xcite and, more recently, question answering @xcite @xcite . however, since amr graphs for such applications are obtained automatically through an amr parser, the benefits of amr integration are highly correlated with the performance of the underlying parser across various data distributions and domains. in recent years, amr parsing and generation models have become more reliable than they used to be, thanks to both the availability of pretrained language models @xcite and the continuous improvements in the amr-specific model architectures @xcite @xcite . however, most of the existing models make use of cumbersome, data-specific techniques and components which not only limit the out-of-distribution generalizability, but also make it difficult to integrate such models in the pipeline of downstream applications. in our recent paper, spring @xcite , we proposed a solution through a simple, end-to-end approach with no heavy inbuilt data processing assumptions. our model achieved unprecedented performance in amr parsing and generation, both in-and out-ofdistribution. to make spring accessible to the community, thereby lowering the entry point to amr applica-tion research, we present spring online services which include: ## spring in this section we revisit the details of spring as in @xcite along with the alterations we employ for this demonstration. ## spring online services here we describe the functionalities of the web interface (section 3.1) and those of the restful apis (section 3.2). we further provide the architectural details and libraries used in appendix a. ## evaluation for the purposes of this demo, we examine different variants of spring to ensure: i) high performance, ii) high generalizability across domains, and iii) efficient and light spring online services. ## related work with a view to demonstrating the progress made in amr, over the years different web services for state-of-the-art amr systems @xcite @xcite have been developed. similarly to spring, konstas et al. (2017), proposed an encoder-decoder system to perform both parsing and generation by relying on data augmentation techniques. this system is associated with a demo foot_4 to only parse into or generate from amr and does not provide extra functionalities, restful apis, or any interaction with the users. similarly, damonte et al. (2017) and damonte and cohen (2019) do not provide other functionalities in their demos beside parsing (damonte et al., 2017, amreager) foot_5 and generation (damonte and cohen, 2019, amrgen) foot_6 . however, spring outperforms the aforementioned systems by more than 20 points in both, smatch for amr parsing and bleu for amr generation. in addition, through spring online services we provide a highly-interactive web interface, restful apis, and the feedback mechanism. ## conclusion with this paper we make available spring online services, with which we bring state-of-theart amr systems into the hands of the community, providing a highly interactive interface and easily integrable apis. our spring system ob-tains results in the same ballpark as the state of the art in both amr-to-text and text-to-amr, using the same weights for both. moreover, our model obtains very strong results in the biomedical domain, providing a powerful tool for building applications. in the future, we intend to extend the services across languages following techniques as in @xcite and @xcite . we make spring online services available at http://nlp.uniroma1.it/spring .