5 papers
Uncertainty Quantification for Evaluating Machine Translation Bias
Ieva Raminta Staliūnaitė, Julius Cheng, Andreas Vlachos
The predictive uncertainty of machine translation (MT) models is typically used as a quality estimation proxy. In this work, we posit that apart from confidently translating when a…
ByteSpan: Information-Driven Subword Tokenisation
Zébulon Goriely, Suchir Salhan, Pietro Lesci +2
Recent dynamic tokenisation methods operate directly on bytes and pool their latent representations into patches. This bears similarities to computational models of word segmentati…
Early-Exit and Instant Confidence Translation Quality Estimation
Vilém Zouhar, Maike Züfle, Beni Egressy +3
Quality estimation is omnipresent in machine translation, for both evaluation and generation. Unfortunately, quality estimation models are often opaque and computationally expensiv…
A Bayesian Optimization Approach to Machine Translation Reranking
Julius Cheng, Maike Züfle, Vilém Zouhar +1
Reranking a list of candidates from a machine translation system with an external scoring model and returning the highest-scoring candidate remains a simple and effective method fo…
Faster Minimum Bayes Risk Decoding with Confidence-based Pruning
Julius Cheng, Andreas Vlachos
Minimum Bayes risk (MBR) decoding outputs the hypothesis with the highest expected utility over the model distribution for some utility function. It has been shown to improve accur…