Christian Heumann


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Automatic Transcription of Handwritten Old Occitan Language
Esteban Garces Arias | Vallari Pai | Matthias Schöffel | Christian Heumann | Matthias Aßenmacher
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

While existing neural network-based approaches have shown promising results in Handwritten Text Recognition (HTR) for high-resource languages and standardized/machine-written text, their application to low-resource languages often presents challenges, resulting in reduced effectiveness. In this paper, we propose an innovative HTR approach that leverages the Transformer architecture for recognizing handwritten Old Occitan language. Given the limited availability of data, which comprises only word pairs of graphical variants and lemmas, we develop and rely on elaborate data augmentation techniques for both text and image data. Our model combines a custom-trained Swin image encoder with a BERT text decoder, which we pre-train using a large-scale augmented synthetic data set and fine-tune on the small human-labeled data set. Experimental results reveal that our approach surpasses the performance of current state-of-the-art models for Old Occitan HTR, including open-source Transformer-based models such as a fine-tuned TrOCR and commercial applications like Google Cloud Vision. To nurture further research and development, we make our models, data sets, and code publicly available.

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A tailored Handwritten-Text-Recognition System for Medieval Latin
Philipp Koch | Gilary Vera Nuñez | Esteban Garces Arias | Christian Heumann | Matthias Schöffel | Alexander Häberlin | Matthias Assenmacher
Proceedings of the Ancient Language Processing Workshop

The Bavarian Academy of Sciences and Humanities aims to digitize the Medieval Latin Dictionary. This dictionary entails record cards referring to lemmas in medieval Latin, a low-resource language. A crucial step of the digitization process is the handwritten text recognition (HTR) of the handwritten lemmas on the record cards. In our work, we introduce an end-to-end pipeline, tailored for the medieval Latin dictionary, for locating, extracting, and transcribing the lemmas. We employ two state-of-the-art image segmentation models to prepare the initial data set for the HTR task. Further, we experiment with different transformer-based models and conduct a set of experiments to explore the capabilities of different combinations of vision encoders with a GPT-2 decoder. Additionally, we also apply extensive data augmentation resulting in a highly competitive model. The best-performing setup achieved a character error rate of 0.015, which is even superior to the commercial Google Cloud Vision model, and shows more stable performance.


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Pre-trained language models evaluating themselves - A comparative study
Philipp Koch | Matthias Aßenmacher | Christian Heumann
Proceedings of the Third Workshop on Insights from Negative Results in NLP

Evaluating generated text received new attention with the introduction of model-based metrics in recent years. These new metrics have a higher correlation with human judgments and seemingly overcome many issues of previous n-gram based metrics from the symbolic age. In this work, we examine the recently introduced metrics BERTScore, BLEURT, NUBIA, MoverScore, and Mark-Evaluate (Petersen). We investigate their sensitivity to different types of semantic deterioration (part of speech drop and negation), word order perturbations, word drop, and the common problem of repetition. No metric showed appropriate behaviour for negation, and further none of them was overall sensitive to the other issues mentioned above.

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CC-Top: Constrained Clustering for Dynamic Topic Discovery
Jann Goschenhofer | Pranav Ragupathy | Christian Heumann | Bernd Bischl | Matthias Aßenmacher
Proceedings of the First Workshop on Ever Evolving NLP (EvoNLP)

Research on multi-class text classification of short texts mainly focuses on supervised (transfer) learning approaches, requiring a finite set of pre-defined classes which is constant over time. This work explores deep constrained clustering (CC) as an alternative to supervised learning approaches in a setting with a dynamically changing number of classes, a task we introduce as dynamic topic discovery (DTD).We do so by using pairwise similarity constraints instead of instance-level class labels which allow for a flexible number of classes while exhibiting a competitive performance compared to supervised approaches. First, we substantiate this through a series of experiments and show that CC algorithms exhibit a predictive performance similar to state-of-the-art supervised learning algorithms while requiring less annotation effort. Second, we demonstrate the overclustering capabilities of deep CC for detecting topics in short text data sets in the absence of the ground truth class cardinality during model training. Third, we showcase that these capabilities can be leveraged for the DTD setting as a step towards dynamic learning over time and finally, we release our codebase to nurture further research in this area.


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Benchmarking down-scaled (not so large) pre-trained language models
Matthias Aßenmacher | Patrick Schulze | Christian Heumann
Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)

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How to Estimate Continuous Sentiments From Texts Using Binary Training Data
Sandra Wankmüller | Christian Heumann
Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)