20 citations · 21 across the 5 of their papers we have counts for
6 papers · 1 filter
Structure-Conditional Minimum Bayes Risk Decoding
Bryan Eikema, Anna Rutkiewicz, Mario Giulianelli
Minimum Bayes Risk (MBR) decoding has seen renewed interest as an alternative to traditional generation strategies. While MBR has proven effective in machine translation, where the…
Teaching Language Models to Faithfully Express their Uncertainty
Bryan Eikema, Evgenia Ilia, José G. C. de Souza +2
Large language models (LLMs) often miscommunicate their uncertainty: repeated queries can produce divergent answers, yet generated responses are typically unhedged or hedged in way…
Findings of the WMT 2024 Shared Task on Chat Translation
Wafaa Mohammed, Sweta Agrawal, M. Amin Farajian +4
This paper presents the findings from the third edition of the Chat Translation Shared Task. As with previous editions, the task involved translating bilingual customer support con…
Sampling-Based Approximations to Minimum Bayes Risk Decoding for Neural Machine Translation
Bryan Eikema, Wilker Aziz
In NMT we search for the mode of the model distribution to form predictions. The mode and other high-probability translations found by beam search have been shown to often be inade…
Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation
Bryan Eikema, Wilker Aziz
Recent studies have revealed a number of pathologies of neural machine translation (NMT) systems. Hypotheses explaining these mostly suggest there is something fundamentally wrong…
Auto-Encoding Variational Neural Machine Translation
Bryan Eikema, Wilker Aziz
We present a deep generative model of bilingual sentence pairs for machine translation. The model generates source and target sentences jointly from a shared latent representation…