46 citations · 50 across the 14 of their papers we have counts for
15 papers
RUMLEM: A Dictionary-Based Lemmatizer for Romansh
Dominic P. Fischer, Zachary Hopton, Jannis Vamvas
Lemmatization -- the task of mapping an inflected word form to its dictionary form -- is a crucial component of many NLP applications. In this paper, we present RUMLEM, a lemmatize…
Robust Language Identification for Romansh Varieties
Charlotte Model, Sina Ahmadi, Jannis Vamvas
The Romansh language has several regional varieties, called idioms, which sometimes have limited mutual intelligibility. Despite this linguistic diversity, there has been a lack of…
SwissGov-RSD: A Human-annotated, Cross-lingual Benchmark for Token-level Recognition of Semantic Differences Between Related Documents
Michelle Wastl, Jannis Vamvas, Rico Sennrich
Recognizing semantic differences across documents is crucial for text generation evaluation and content alignment, especially in cross-lingual settings. However, as a standalone ta…
QueST: Incentivizing LLMs to Generate Difficult Problems
Hanxu Hu, Xingxing Zhang, Jannis Vamvas +2
Large Language Models have achieved strong performance on reasoning tasks, solving competition-level coding and math problems. However, their scalability is limited by human-labele…
The Mediomatix Corpus: Parallel Data for Romansh Language Varieties via Comparable Schoolbooks
Zachary Hopton, Jannis Vamvas, Andrin Büchler +3
The five idioms (i.e., varieties) of the Romansh language are largely standardized and are taught in the schools of the respective communities in Switzerland. In this paper, we pre…
20min-XD: A Comparable Corpus of Swiss News Articles
Michelle Wastl, Jannis Vamvas, Selena Calleri +1
We present 20min-XD (20 Minuten cross-lingual document-level), a French-German, document-level comparable corpus of news articles, sourced from the Swiss online news outlet 20 Minu…
Source-primed Multi-turn Conversation Helps Large Language Models Translate Documents
Hanxu Hu, Jannis Vamvas, Rico Sennrich
LLMs have paved the way for truly simple document-level machine translation, but challenges such as omission errors remain. In this paper, we study a simple method for handling doc…
Investigating Multi-Pivot Ensembling with Massively Multilingual Machine Translation Models
Alireza Mohammadshahi, Jannis Vamvas, Rico Sennrich
Massively multilingual machine translation models allow for the translation of a large number of languages with a single model, but have limited performance on low- and very-low-re…
Mitigating Hallucinations and Off-target Machine Translation with Source-Contrastive and Language-Contrastive Decoding
Rico Sennrich, Jannis Vamvas, Alireza Mohammadshahi
Hallucinations and off-target translation remain unsolved problems in MT, especially for low-resource languages and massively multilingual models. In this paper, we introduce two r…
Towards Unsupervised Recognition of Token-level Semantic Differences in Related Documents
Jannis Vamvas, Rico Sennrich
Automatically highlighting words that cause semantic differences between two documents could be useful for a wide range of applications. We formulate recognizing semantic differenc…
SwissBERT: The Multilingual Language Model for Switzerland
Jannis Vamvas, Johannes Graën, Rico Sennrich
We present SwissBERT, a masked language model created specifically for processing Switzerland-related text. SwissBERT is a pre-trained model that we adapted to news articles writte…
NMTScore: A Multilingual Analysis of Translation-based Text Similarity Measures
Jannis Vamvas, Rico Sennrich
Being able to rank the similarity of short text segments is an interesting bonus feature of neural machine translation. Translation-based similarity measures include direct and piv…
As Little as Possible, as Much as Necessary: Detecting Over- and Undertranslations with Contrastive Conditioning
Jannis Vamvas, Rico Sennrich
Omission and addition of content is a typical issue in neural machine translation. We propose a method for detecting such phenomena with off-the-shelf translation models. Using con…
On the Limits of Minimal Pairs in Contrastive Evaluation
Jannis Vamvas, Rico Sennrich
Minimal sentence pairs are frequently used to analyze the behavior of language models. It is often assumed that model behavior on contrastive pairs is predictive of model behavior…
X-Stance: A Multilingual Multi-Target Dataset for Stance Detection
Jannis Vamvas, Rico Sennrich
We extract a large-scale stance detection dataset from comments written by candidates of elections in Switzerland. The dataset consists of German, French and Italian text, allowing…