We present a study on the automatic classification of speech acts in the domain of political communication, based on J. R. Searle's classification of illocutionary acts.Our research involves creating a dataset using the US State of the Union corpus and the UN General Debate corpus (UNGD) as data sources.To overcome limited labelled data, we employ a combination of weak supervision and active learning techniques for dataset creation and model training.Through various experiments, we investigate the influence of external and internal factors on speech act classification.In addition, we discuss the potential for further analysis of speech act usage, using the trained model on the UNGD corpus.The findings demonstrate the effectiveness of Transformer-based models for automatic speech act classification, highlight the benefits of weak supervision and active learning for dataset creation and model training, and underscore the potential for large-scale statistical analysis of speech act usage in the domain of political communication.

Christopher Schröder, Kim Bürgl, Yves Annanias, Andreas Niekler, Lydia Müller, Daniel Wiegreffe, Christian Bender, Christoph Mengs, Gerik Scheuermann, Gerhard Heyer. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

We present the IR Anthology, a corpus of information retrieval publications accessible via a metadata browser and a full-text search engine. Following the example of the well-known ACL Anthology, the IR Anthology serves as a hub for researchers interested in information retrieval. Our search engine ChatNoir indexes the publications' full texts, enabling a focused search and linking users to the respective publisher's site for personal access. Listing more than 40,000 publications at the time of writing, the IR Anthology can be freely accessed at https://IR.webis.de.