Anne Göhring

Also published as: Anne Goehring


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Machine vs. Human: Exploring Syntax and Lexicon in German Translations, with a Spotlight on Anglicisms
Anastassia Shaitarova | Anne Göhring | Martin Volk
Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)

Machine Translation (MT) has become an integral part of daily life for millions of people, with its output being so fluent that users often cannot distinguish it from human translation. However, these fluid texts often harbor algorithmic traces, from limited lexical choices to societal misrepresentations. This raises concerns about the possible effects of MT on natural language and human communication and calls for regular evaluations of machine-generated translations for different languages. Our paper explores the output of three widely used engines (Google, DeepL, Microsoft Azure) and one smaller commercial system. We translate the English and French source texts of seven diverse parallel corpora into German and compare MT-produced texts to human references in terms of lexical, syntactic, and morphological features. Additionally, we investigate how MT leverages lexical borrowings and analyse the distribution of anglicisms across the German translations.

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An Open-Source Gloss-Based Baseline for Spoken to Signed Language Translation
Amit Moryossef | Mathias Müller | Anne Göhring | Zifan Jiang | Yoav Goldberg | Sarah Ebling
Proceedings of the Second International Workshop on Automatic Translation for Signed and Spoken Languages

Sign language translation systems are complex and require many components. As a result, it is very hard to compare methods across publications. We present an open-source implementation of a text-to-gloss-to-pose-to-video pipeline approach, demonstrating conversion from German to Swiss German Sign Language, French to French Sign Language of Switzerland, and Italian to Italian Sign Language of Switzerland. We propose three different components for the text-to-gloss translation: a lemmatizer, a rule-based word reordering and dropping component, and a neural machine translation system. Gloss-to-pose conversion occurs using data from a lexicon for three different signed languages, with skeletal poses extracted from videos. To generate a sentence, the text-to-gloss system is first run, and the pose representations of the resulting signs are stitched together.

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Gender-tailored Semantic Role Profiling for German
Manfred Klenner | Anne Göhring | Alison Kim | Dylan Massey
Proceedings of the 15th International Conference on Computational Semantics

In this short paper, we combine the semantic perspective of particular verbs as casting a positive or negative relationship between their role fillers with a pragmatic examination of how the distribution of particular vulnerable role filler subtypes (children, migrants, etc.) looks like. We focus on the gender subtype and strive to extract gender-specific semantic role profiles: who are the predominant sources and targets of which polar events - men or women. Such profiles might reveal gender stereotypes or biases (of the media), but as well could be indicative of our social reality.

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Findings of the Second WMT Shared Task on Sign Language Translation (WMT-SLT23)
Mathias Müller | Malihe Alikhani | Eleftherios Avramidis | Richard Bowden | Annelies Braffort | Necati Cihan Camgöz | Sarah Ebling | Cristina España-Bonet | Anne Göhring | Roman Grundkiewicz | Mert Inan | Zifan Jiang | Oscar Koller | Amit Moryossef | Annette Rios | Dimitar Shterionov | Sandra Sidler-Miserez | Katja Tissi | Davy Van Landuyt
Proceedings of the Eighth Conference on Machine Translation

This paper presents the results of the Second WMT Shared Task on Sign Language Translation (WMT-SLT23; This shared task is concerned with automatic translation between signed and spoken languages. The task is unusual in the sense that it requires processing visual information (such as video frames or human pose estimation) beyond the well-known paradigm of text-to-text machine translation (MT). The task offers four tracks involving the following languages: Swiss German Sign Language (DSGS), French Sign Language of Switzerland (LSF-CH), Italian Sign Language of Switzerland (LIS-CH), German, French and Italian. Four teams (including one working on a baseline submission) participated in this second edition of the task, all submitting to the DSGS-to-German track. Besides a system ranking and system papers describing state-of-the-art techniques, this shared task makes the following scientific contributions: novel corpora and reproducible baseline systems. Finally, the task also resulted in publicly available sets of system outputs and more human evaluation scores for sign language translation.


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Semantic Role Labeling for Sentiment Inference: A Case Study
Manfred Klenner | Anne Göhring
Proceedings of the 18th Conference on Natural Language Processing (KONVENS 2022)

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Animacy Denoting German Nouns: Annotation and Classification
Manfred Klenner | Anne Göhring
Proceedings of the Thirteenth Language Resources and Evaluation Conference

In this paper, we introduce a gold standard for animacy detection comprising almost 14,500 German nouns that might be used to denote either animate entities or non-animate entities. We present inter-annotator agreement of our crowd-sourced seed annotations (9,000 nouns) and discuss the results of machine learning models applied to this data.

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Polar Quantification of Actor Noun Phrases for German
Anne Göhring | Manfred Klenner
Proceedings of the Thirteenth Language Resources and Evaluation Conference

In this paper, we discuss work that strives to measure the degree of negativity - the negative polar load - of noun phrases, especially those denoting actors. Since no gold standard data is available for German for this quantification task, we generated a silver standard and used it to fine-tune a BERT-based intensity regressor. We evaluated the quality of the silver standard empirically and found that our lexicon-based quantification metric showed a strong correlation with human annotators.


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DeInStance: Creating and Evaluating a German Corpus for Fine-Grained Inferred Stance Detection
Anne Göhring | Manfred Klenner | Sophia Conrad
Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)

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Getting Hold of Villains and other Rogues
Manfred Klenner | Anne Göhring | Sophia Conrad
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

In this paper, we introduce the first corpus specifying negative entities within sentences. We discuss indicators for their presence, namely particular verbs, but also the linguistic conditions when their prediction should be suppressed. We further show that a fine-tuned Bert-based baseline model outperforms an over-generating rule-based approach which is not aware of these further restrictions. If a perfect filter were applied, both would be on par.


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Encoder-Decoder Methods for Text Normalization
Massimo Lusetti | Tatyana Ruzsics | Anne Göhring | Tanja Samardžić | Elisabeth Stark
Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

Text normalization is the task of mapping non-canonical language, typical of speech transcription and computer-mediated communication, to a standardized writing. It is an up-stream task necessary to enable the subsequent direct employment of standard natural language processing tools and indispensable for languages such as Swiss German, with strong regional variation and no written standard. Text normalization has been addressed with a variety of methods, most successfully with character-level statistical machine translation (CSMT). In the meantime, machine translation has changed and the new methods, known as neural encoder-decoder (ED) models, resulted in remarkable improvements. Text normalization, however, has not yet followed. A number of neural methods have been tried, but CSMT remains the state-of-the-art. In this work, we normalize Swiss German WhatsApp messages using the ED framework. We exploit the flexibility of this framework, which allows us to learn from the same training data in different ways. In particular, we modify the decoding stage of a plain ED model to include target-side language models operating at different levels of granularity: characters and words. Our systematic comparison shows that our approach results in an improvement over the CSMT state-of-the-art.


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Building a Spanish-German Dictionary for Hybrid MT
Anne Göhring
Proceedings of the 3rd Workshop on Hybrid Approaches to Machine Translation (HyTra)


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Machine Learning Disambiguation of Quechua Verb Morphology
Annette Rios Gonzales | Anne Göhring
Proceedings of the Second Workshop on Hybrid Approaches to Translation


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A tree is a Baum is an árbol is a sach’a: Creating a trilingual treebank
Annette Rios | Anne Göhring
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

This paper describes the process of constructing a trilingual parallel treebank. While for two of the involved languages, Spanish and German, there are already corpora with well-established annotation schemes available, this is not the case with the third language: Cuzco Quechua (ISO 639-3:quz), a low-resourced, non-standardized language for which we had to define a linguistically plausible annotation scheme first.


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Le corpus Text+Berg Une ressource parallèle alpin français-allemand (The Text+Berg Corpus An Alpine French-German Parallel Resource)
Anne Göhring | Martin Volk
Actes de la 18e conférence sur le Traitement Automatique des Langues Naturelles. Articles courts

Cet article présente un corpus parallèle français-allemand de plus de 4 millions de mots issu de la numérisation d’un corpus alpin multilingue. Ce corpus est une précieuse ressource pour de nombreuses études de linguistique comparée et du patrimoine culturel ainsi que pour le développement d’un système statistique de traduction automatique dans un domaine spécifique. Nous avons annoté un échantillon de ce corpus parallèle et aligné les structures arborées au niveau des mots, des constituants et des phrases. Cet “alpine treebank” est le premier corpus arboré parallèle français-allemand de haute qualité (manuellement contrôlé), de libre accès et dans un domaine et un genre nouveau : le récit d’alpinisme.


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Combining Parallel Treebanks and Geo-Tagging
Martin Volk | Anne Goehring | Torsten Marek
Proceedings of the Fourth Linguistic Annotation Workshop