activity
20162022
most citedHow Language-Neutral is Multilingual BERT?

76 citations · 91 across the 11 of their papers we have counts for

collaborators

18 papers

cs.CL20223 cited

: 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…

cs.CL20221 cited

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…

cs.DB2022

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…

cs.CL20221 cited

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…

cs.CL20226 cited

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…

cs.CL2022

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…