Yannick Frommherz

Research Associate

at the Chair of Applied Linguistics

© Yannick Frommherz

Office hours during the lecture period

By arrangement at https://tu-dresden.zoom.us/j/65007057407?pwd=NlNWL0t6YXNCbHZpWlY4cWtSRTNUZz09

Office hours during the lecture-free period

By arrangement at https://tu-dresden.zoom.us/j/65007057407?pwd=NlNWL0t6YXNCbHZpWlY4cWtSRTNUZz09

Vita

Dissertation project

Alignment describes the psycholinguistic phenomenon that interlocutors align with each other on different linguistic levels within an interaction, e.g. on the lexical level: If speaker A introduces the expression x to reference an entity, it is likely that her interlocutor B will take up the same expression x to refer to the same entity (Pickering & Garrod, 2004; Szmrecanyi, 2005; Koulouri et al., 2016). This tendency has been shown for both human-human interaction (Branigan et al., 2000) and human-machine interaction, including for lexis (Branigan & Pearson, 2016; Huiyang & Min, 2022), syntax (Heyselaar et al., 2017) and prosody (Raveh et al, 2019), as well as for various settings such as interactions with chatbots (Lotze, 2016), robots (Fischer, 2016) and voice assistants (Linnemann & Jucks, 2018).

The majority of research on alignment in human-machine interaction is of an experimental nature, whereby the so-called picture-naming-matching task is predominantly used, usually as a wizard-of-oz setup. Test subjects and the machine (or a human disguised as a machine, i.e. a wizard) alternately exchange descriptions of pictures, which the other has to assign to one of several pictures. The machine's description conceals linguistic elements (e.g. a passive construction) to which the respondent then (unconsciously) aligns or does not align in her description (Pearson et al., 2006, Heyselaar et al., 2017). While this design is well controllable, it emulates a scenario that is far removed from real human-machine interaction, which reduces the ecological validity of such findings. On the other hand, a corpus-based approach cannot directly demonstrate alignment processes in the brain, but can only show the persistence of linguistic structures (Lotze, 2016). At the same time, it offers the advantage that (reasonably) authentic interactions between humans and actual machines can be evaluated. Nevertheless, there is hardly any corpus-based research on alignment in human-machine interaction. A laudable exception is Lotze (2016), who analyzed the topic in interactions between humans and chatbots and found extensive evidence for lexical and syntactic persistence. What is still completely lacking is corpus-based research on persistence in interactions between humans and voice assistants, which are becoming increasingly widespread in the form of Siri and Alexa (Byrne et al., 2019; Yuan et al., 2020). This dissertation project aims to fill this research gap by analyzing several German-language corpora with interactions between humans and Alexa for persistence.

Publications

5 Entries

2024

• And then I saw it: Testing Hypotheses on Turning Points in a Corpus of UFO Sighting ReportsLangenhorst, J., Schuppe, R. C. & Frommherz, Y., 2024Research output: Contribution to conferences > Paper

2023

• Keyness in song lyrics: Challenges of highly clumpy dataLangenhorst, J., Frommherz, Y. & Meier-Vieracker, S., 1 May 2023, In: Journal for Language Technology and Computational Linguistics. 36, 1, p. 21-38, 18 p.Electronic (full-text) versionResearch output: Contribution to journal > Research article

2022

• Digitale Kompetenzen für Geistes- und Sozialwissenschaftler:innen. Vorzüge eines Blended Learning-Formats für die Vermittlung von Programmierkenntnissen.Frommherz, Y. & Langenhorst, J., 5 Jul 2022, In: Lessons Learned. 2, 1, 12 p.Electronic (full-text) versionResearch output: Contribution to journal > Research article

2021

• Thinking Things in German versus Swedish. A Cross-Linguistic comparison of Verbs of Thinking in Two Genetically Close Languages.Frommherz, Y., 11 Nov 2021, In:  Studia linguistica : a journal of general linguistics. 76, 2, p. 464-506, 43 p.Electronic (full-text) versionResearch output: Contribution to journal > Research article
• Crowdsourcing Ecologically-Valid Dialogue Data for GermanFrommherz, Y. & Zarcone, A., 21 Jun 2021, In: Frontiers in Computer Science. 3, 686050Electronic (full-text) versionResearch output: Contribution to journal > Research article

This information is provided by the FIS.

Presentations

12/2023 Alignment in human-language assistant interaction (Artificial Friday. Linguistic perspectives on artificial intelligence, December 01, 2023) https://artificial-friday.de/yannick-frommherz-dresden/#info

09/2023 Human-language assistant interaction between naturalness and error-proneness (Annual Conference of the Society for Applied Linguistics, Section Media Communication, September 20 - 22, 2023, Johannes Gutenberg University Mainz) https://www.gal2023.de/

03/2023 "Hello Humanities!" - A Modular Python Programming Course Targeting the Specific Needs and Requirements of Humanities Students (Conference: Programming an Data Infrastructure in Digital Humanities, March 27 - 29, 2023, Universidade de Évora, Portugal) https://indico.hpc.uevora.pt/event/36/

11/2022 Thinking Things in German versus Swedish. A Cross-Linguistic comparison of Verbs of Thinking in Two Genetically Close Languages (Invited speaker at the Grammar Seminar of the Language and Literature Center at Lund University, Sweden) https://www.sol.lu.se/sol/kalendarium/evenemang/grammatikseminarietthe-grammar-seminar-yannick-frommherz-dresden-thinking-things-german-versus

Teaching

Research interests

• Digital linguistics

• Interactional linguistics

• Human-machine interaction