Steffen Herbold


2025

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From Isolates to Families: Using Neural Networks for Automated Language Affiliation
Frederic Blum | Steffen Herbold | Johann-Mattis List
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In historical linguistics, the affiliation of languages to a common language family is traditionally carried out using a complex workflow that relies on manually comparing individual languages. Large-scale standardized collections of multilingual wordlists and grammatical language structures might help to improve this and open new avenues for developing automated language affiliation workflows. Here, we present neural network models that use lexical and grammatical data from a worldwide sample of more than 1,200 languages with known affiliations to classify individual languages into families. In line with the traditional assumption of most linguists, our results show that models trained on lexical data alone outperform models solely based on grammatical data, whereas combining both types of data yields even better performance. In additional experiments, we show how our models can identify long-ranging relations between entire subgroups, how they can be employed to investigate potential relatives of linguistic isolates, and how they can help us to obtain first hints on the affiliation of so far unaffiliated languages. We conclude that models for automated language affiliation trained on lexical and grammatical data provide comparative linguists with a valuable tool for evaluating hypotheses about deep and unknown language relations.

2024

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Question Type Prediction in Natural Debate
Zlata Kikteva | Alexander Trautsch | Steffen Herbold | Annette Hautli-Janisz
Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue

In spontaneous natural debate, questions play a variety of crucial roles: they allow speakers to introduce new topics, seek other speakers’ opinions or indeed confront them. A three-class question typology has previously been demonstrated to effectively capture details pertaining to the nature of questions and the different functions associated with them in a debate setting. We adopt this classification and investigate the performance of several machine learning approaches on this task by incorporating various sets of lexical, dialogical and argumentative features. We find that BERT demonstrates the best performance on the task, followed by a Random Forest model enriched with pragmatic features.

2023

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On the Impact of Reconstruction and Context for Argument Prediction in Natural Debate
Zlata Kikteva | Alexander Trautsch | Patrick Katzer | Mirko Oest | Steffen Herbold | Annette Hautli-Janisz
Proceedings of the 10th Workshop on Argument Mining

Debate naturalness ranges on a scale from small, highly structured, and topically focused settings to larger, more spontaneous and less constrained environments. The more unconstrained a debate, the more spontaneous speakers act: they build on contextual knowledge and use anaphora or ellipses to construct their arguments. They also use rhetorical devices such as questions and imperatives to support or attack claims. In this paper, we study how the reconstruction of the actual debate contributions, i.e., utterances which contain pronouns, ellipses and fuzzy language, into full-fledged propositions which are interpretable without context impacts the prediction of argument relations and investigate the effect of incorporating contextual information for the task. We work with highly complex spontaneous debates with more than 10 speakers on a wide variety of topics. We find that in contrast to our initial hypothesis, reconstruction does not improve predictions and context only improves them when used in combination with propositions.