Text Technology/Digital Linguistics Colloquium HS 2026

Time & Location: every 2 weeks on Tuesdays 10:15-12:00 in room BIM 4-033.

Online participation via the MS Teams Team CL Colloquium is also possible.

Colloquium Schedule

15.09.2026	Mark Fišel	Jana Massoud & Sina Ahmadi
29.09.2026	Yiyang Chen	Tanja Samardžić
13.10.2026	Anna Bondar	Michelle Wastl
27.10.2026	Cui Ding	Jan Brasser
10.11.2026	Bastian Rieck	Jason Armitage
24.11.2026	Hanxu Hu	Andrianos Michail
08.12.2026	Longqian Ming	Gerard Sant Muniesa

15 Sep 2026

Mark Fišel: Modelling Extremely Low-resource Finno-Ugric Languages and Dialects

I will describe an on-going effort on developing NLP tools for the Finno-Ugric family, the members of which range from languages with 1M+ speakers and "an army and fleet" to critically endangered dialects and languages with few resources and even fewer speakers. The latest development direction in this work is on multi-tasking models and general-purpose LMs, which is, however, hindered by the availability of data, low domain coverage, diverse quality in data from different sources and variety in orthography. I will present the already developed models and performed experiments, and will be happy to discuss open questions.

Jana Massoud & Sina Ahmadi: Towards a quantitative language resource index

The term "low-resource" in NLP is too vague to be useful. This talk presents our project that aims to move towards a quantitative resource index that scores 8,168 languages across six key dimensions (from corpora and models to dictionaries). Our analysis reveals a heavily skewed landscape where 75% of the world's languages are entirely absent from contemporary NLP. We validate this index against downstream tasks, offering a new, fine-grained tool to accurately situate languages in the tech ecosystem and identify where resources are needed most.

29 Sep 2026

Yiyang Chen: Towards Automatic, Label-Free Expressive TTS

How can a text-to-speech (TTS) system infer an appropriate emotional expression from text and use it to guide speech synthesis? This talk explores automatic emotion prediction and control in zero-shot TTS. I will introduce our progression from selecting emotional speech references using predicted valence, arousal, and dominance (VAD) to EmoPilot, which predicts emotion cues directly in the TTS backend’s learned emotion space. These cues support reference-audio retrieval or direct conditioning, bypassing explicit emotion-category and VAD predictions. I will then discuss our next steps towards context-aware emotion prediction: the same sentence can convey different emotions depending on its surrounding context. Our upcoming Romansh long-form audio generation project provides a motivating setting, with training speech limited in both quantity and emotional expressiveness. Can emotional expression learned from speech in other languages transfer to Romansh synthesis? Reference and synthesized audio examples will illustrate the research questions and provide a starting point for discussing future directions and applications.

Tanja Samardžić: Controlled token allocation in optimising multilingual vocabularies

Multilingual (pre-)training of large language models is necessary but it is prone to various biases due to the uneven data availability and structural differences between languages. This problem becomes apparent already at the level of tokenisation: if no care is taken, a multilingual vocabulary (constructed by means of BPE) ends up dominated by items representing high-resource languages. To prevent this, various controlling techniques have been proposed, from upsampling tokens from low-resource languages to minimising token sharing across languages and setting cross-lingual parity as one of the BPE's objectives. In this talk, I will present experiments testing potential advantages of several new techniques aimed at designing better multilingual vocabularies. Our preliminary findings suggest that controlled token allocation is more beneficial with larger vocabularies allowing systematic improvements in model-intrinsic metrics (bits-per-byte, information parity) and also in some downstream tasks (belebele).

13 Oct 2026

Anna Bondar: Mixture-of-Readers (MoRe): Enabling Scanpath Generation for Different Reader Groups

Eye movements during reading constitute a rich behavioural signal of moment-to-moment cognitive processing. As such, they have long served as a central dependent variable in psycholinguistic research, and, recently, have been leveraged for cognitively enhancing language models. However, eye-tracking data are scarce, which has motivated the development of deep learning models of eye movements during reading. Previous work has shown that these models benefit from training on multiple reader groups, partly because this increases the amount of training data. A central limitation of previous approaches, however, is that, despite being trained on heterogeneous data, the models generate overly homogeneous scanpaths and fail to capture group-specific reading patterns. Preserving such group-level differences is important for both psycholinguistic and NLP research. In this paper, we introduce Mixture-of-Readers (MoRe), a model based on a mixture-of-experts architecture for generating group-specific scanpaths. We experiment with different routing strategies and evaluate our approach by predicting scanpaths for dyslexic and neurotypical L1, as well as L2 readers across English and Danish languages. We evaluate the models in terms of predictive performance and their ability to preserve scanpath diversity between and within reader groups. We further analyse the expert selection patterns across the reader groups.

Michelle Wastl: Fine-grained Text Comparison: Scaling Word Alignment to Documents and Benchmarking Token Representations

Tasks such as the recognition of semantic differences (RSD), hallucination detection, or label projection require systems to compare two texts at the level of individual tokens. For such tasks, we have found simple word alignment based on embedding similarity to outperform LLM prompting at a fraction of the computational cost.
In the first part of this talk, I will present CTFAlign, a coarse-to-fine approach that extends unsupervised embedding-based word alignment from sentences to full document pairs without relying on sentence segmentation. I will also show how it transfers to several fine-grained text comparison tasks.
How well such methods work depends on the choice of encoder, which is usually guided by text embedding benchmarks such as MTEB. These benchmarks, however, evaluate pooled text representations and therefore say little about the quality of individual token representations, which is what fine-grained comparison relies on. Hence, in the second part of the talk, I will present an ongoing project that brings existing fine-grained text comparison datasets together into a single benchmark for token-level representations.