76 citations · 148 across the 26 of their papers we have counts for
10 papers · 1 filter
Speaking Multiple Languages Affects the Moral Bias of Language Models
Katharina Hämmerl, Björn Deiseroth, Patrick Schramowski +4
Pre-trained multilingual language models (PMLMs) are commonly used when dealing with data from multiple languages and cross-lingual transfer. However, PMLMs are trained on varying…
: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter
Wen Lai, Alexandra Chronopoulou, Alexander Fraser
Multilingual neural machine translation models (MNMT) yield state-of-the-art performance when evaluated on data from a domain and language pair seen at training time. However, when…
A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processing
Sophie Henning, William Beluch, Alexander Fraser +1
Many natural language processing (NLP) tasks are naturally imbalanced, as some target categories occur much more frequently than others in the real world. In such scenarios, curren…
Language-Family Adapters for Low-Resource Multilingual Neural Machine Translation
Alexandra Chronopoulou, Dario Stojanovski, Alexander Fraser
Large multilingual models trained with self-supervision achieve state-of-the-art results in a wide range of natural language processing tasks. Self-supervised pretrained models are…
Don't Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings
Silvia Severini, Viktor Hangya, Masoud Jalili Sabet +2
Bilingual Word Embeddings (BWEs) are one of the cornerstones of cross-lingual transfer of NLP models. They can be built using only monolingual corpora without supervision leading t…
Demonstrating CAT: Synthesizing Data-Aware Conversational Agents for Transactional Databases
Marius Gassen, Benjamin Hättasch, Benjamin Hilprecht +3
Databases for OLTP are often the backbone for applications such as hotel room or cinema ticket booking applications. However, developing a conversational agent (i.e., a chatbot-lik…
Modeling Target-Side Morphology in Neural Machine Translation: A Comparison of Strategies
Marion Weller-Di Marco, Matthias Huck, Alexander Fraser
Morphologically rich languages pose difficulties to machine translation. Machine translation engines that rely on statistical learning from parallel training data, such as state-of…
Do Multilingual Language Models Capture Differing Moral Norms?
Katharina Hämmerl, Björn Deiseroth, Patrick Schramowski +3
Massively multilingual sentence representations are trained on large corpora of uncurated data, with a very imbalanced proportion of languages included in the training. This may ca…
Combining Static and Contextualised Multilingual Embeddings
Katharina Hämmerl, Jindřich Libovický, Alexander Fraser
Static and contextual multilingual embeddings have complementary strengths. Static embeddings, while less expressive than contextual language models, can be more straightforwardly…
Addressing the Challenges of Cross-Lingual Hate Speech Detection
Irina Bigoulaeva, Viktor Hangya, Iryna Gurevych +1
The goal of hate speech detection is to filter negative online content aiming at certain groups of people. Due to the easy accessibility of social media platforms it is crucial to…