Two Decades of the ACL A nthology: Development, Impact, and Open Challenges (2023)

Abstract: The ACL Anthology is a prime resource for research papers within computational linguistics and natural language processing, while continuing to be an open-source and community-driven project. Since Gildea et al. (2018) reported on its state and planned directions, the Anthology has seen major technical changes. We discuss what led to these changes and how they impact long-term maintainability and community engagement, describe which open-source data and software tools the Anthology currently provides, and provide a survey of literature that has used the Anthology as a main data source.

## introduction the acl anthology 1 is a repository for scientific contributions within computational linguistics and natural language processing maintained by the association for computational linguistics (acl). it currently hosts over 88k papers from from relevant conferences and journals within the field, including both acl-sponsored and non-acl venues, nearly 400 in total, a growth of almost 70% since 2019 (see figure 1 ). it also includes many related materials such as software, posters, slides, and recordings of talks. all papers and materials are open-access, provided to the world without barrier under various open licenses. @xcite development of the acl anthology takes place in a public repository, 3 which contains (i) metadata for all items in the anthology, in xml and yaml formats; (ii) code for accessing and transforming this metadata, in form of a python library and scripts; and (iii) code and templates for generating the acl anthology website. all code is made available under the permissive apache-2.0 license. @xcite development has been almost entirely volunteer-driven, though since 2021 the acl has funded assistants who have contributed to ingestions at the rate of about 20 hours a month. in this paper, we first describe the metadata currently provided by the acl anthology and efforts to improve it ( §2); the technical framework and development of the website ( §3); as well as a python library for accessing data from the anthology ( §4). we then look at the impact the acl anthology has had on other open-source software projects and provide a survey of known datasets and studies that rely on the anthology as a main data source ( §5). finally, we discuss lessons and challenges ( §6) as well as future directions that we would like to see realized for the anthology ( §7), for which we rely on help from the community. anderson et al. (2012) as stored in the acl anthology repository. ## publication metadata at the core of the anthology repository is the metadata on the publications it hosts. here, we describe the different kinds of data provided by the anthology, which efforts we have taken to enrich this data and ensure correctness, and how new materials are added to the anthology. ## website most users interact with the acl anthology through its website. here, we describe how the website is built, the technical developments it has seen in recent years, and new features we have introduced for the community. ## python library building the anthology website requires transforming all the metadata described above into a format suitable for generating the html pages using hugo. to do this, we rely on a custombuilt python library that is currently found in the bin/anthology/ folder of the repository. besides wrapping access to the xml and yaml data files, the python library (i) implements author ## impact of the acl anthology a lot of research has built on data from the acl anthology over the years, but even the technical infrastructure has had impact on other open-source projects. here, we try to provide an extensive survey of software, datasets, and scientific studies that directly rely on data or code from the anthology. ## lessons & challenges impact of open development all developmentincluding changes to the data -happening in public is highly beneficial. by far the most common contributions by non-project members are metadata corrections such as name changes and typos. some of these come directly as pull requests (≈1k so far), which can often be merged within a day and reduce the workload of the anthology volunteers; others come as (pre-formatted) github issues (also ≈1k so far), which are then automatically linked to pull requests that are merged monthly. as everyone can build the anthology themselves, we occasionally get feedback or suggestions regarding the robustness of the infrastructure on different systems, but overall contributions to the code parts of the project are rare. one notable exception is the integration with papers with code (pwc): this functionality was suggested and in large parts contributed by the team behind pwc. ## conclusion the acl anthology has grown into an invaluable resource for the cl/nlp community. volunteer contributions are responsible for virtually all of the improvements of the metadata ( §2), the anthology website ( §3), and the provided python library ( §4). the survey in §5 highlights the utility and importance of this resource for academic research. to address the ongoing challenges ( §6) and future directions ( §7), we continue to rely on help from the community. we encourage anyone who is interested in contributing to the acl anthology to explore our github repository. @xcite