We present the first dataset, an annotation scheme, discourse analysis, and baseline experiments on argumentation and domain content types in scholarly articles on political science, specifically on the theory of International Relations (IR). The dataset comprises over 1 600 sentences stemming from three foundational articles on Neo-Realism, Liberalism, and Constructivism. We show that our annotation scheme enables educationally-relevant insight into the scholarly IR discourse and that state-of-the-art classifiers, while effective in distinguishing basic argumentative elements (Claims and Support/Attack relations) reaching up to 0.97 micro F1 , require domain-specific training and fine-tuning on the more fine-grained tasks of relation and content type prediction.

Matti Wiegmann, Magdalena Wolska, Christopher Schröder, Ole Borchardt, Benno Stein, Martin Potthast. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

At least 5% of questions submitted to search engines ask about cause-effect relationships in some way. To support the development of tailored approaches that can answer such questions, we construct Webis-CausalQA-22, a benchmark corpus of 1.1 million causal questions with answers. We distinguish different types of causal questions using a novel typology derived from a data-driven, manual analysis of questions from ten large question answering (QA) datasets. Using high-precision lexical rules, we extract causal questions of each type from these datasets to create our corpus. As an initial baseline, the state-of-the-art QA model UnifiedQA achieves a ROUGE-L F1 score of 0.48 on our new benchmark.

Most work on segmenting text does so on the basis of topic changes, but it can be of interest to segment by other, stylistically expressed characteristics such as change of authorship or native language.We propose a Bayesian unsupervised text segmentation approach to the latter.While baseline models achieve essentially random segmentation on our task, indicating its difficulty, a Bayesian model that incorporates appropriately compact language models and alternating asymmetric priors can achieve scores on the standard metrics around halfway to perfect segmentation.

The present study investigates the voicing contrast in Bulgarian and German. Analyses of two production experiments are reported. In the first experiment logatoms were constructed containing /p, t, k/ and /b, d, g/ in intervocalic position. In the second experiment one Bulgarian and one German sentence were elicited in different focus conditions resulting in different accentuation levels. Based on the obtained data we analyze the phonetic implementation of the phonological categories voiced vs. voiceless and the influence of focus condition and accentuation. It is shown, that: First, the two languages differ in the phonetic realization of /p, t, k/ but not /b, d, g/ in intervocalic position in terms of voice onset time (short lag in Bulgarian and long lag in German). Second, accentuation levels are realised in different ways in the two languages.

Discourse in formal domains, such as mathematics, is characterized by a mixture of telegraphic natural language and embedded (semi-)formal symbolic mathematical expressions. We present language phenomena observed in a corpus of dialogs with a simulated tutorial system for proving theorems as evidence for the need for deep syntactic and semantic analysis. We propose an approach to input understanding in this setting. Our goal is a uniform analysis of inputs of different degree of verbalization: ranging from symbolic alone to fully worded mathematical expressions.