70 citations · 74 across the 5 of their papers we have counts for
5 papers
A comparison of translation performance between DeepL and Supertext
Alex Flückiger, Chantal Amrhein, Tim Graf +5
As strong machine translation (MT) systems are increasingly based on large language models (LLMs), reliable quality benchmarking requires methods that capture their ability to leve…
Turning English-centric LLMs Into Polyglots: How Much Multilinguality Is Needed?
Tannon Kew, Florian Schottmann, Rico Sennrich
The vast majority of today's large language models (LLMs) are English-centric, having been pretrained predominantly on English text. Yet, in order to meet user expectations, models…
A Benchmark for Evaluating Machine Translation Metrics on Dialects Without Standard Orthography
Noëmi Aepli, Chantal Amrhein, Florian Schottmann +1
For sensible progress in natural language processing, it is important that we are aware of the limitations of the evaluation metrics we use. In this work, we evaluate how robust me…
Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Model
Chantal Amrhein, Florian Schottmann, Rico Sennrich +1
Natural language generation models reproduce and often amplify the biases present in their training data. Previous research explored using sequence-to-sequence rewriting models to…
State-of-the-art generalisation research in NLP: A taxonomy and review
Dieuwke Hupkes, Mario Giulianelli, Verna Dankers +17
The ability to generalise well is one of the primary desiderata of natural language processing (NLP). Yet, what 'good generalisation' entails and how it should be evaluated is not…