The field of machine translation (MT), the automatic translation of written text from one
natural language into another, has experienced a major paradigm shift in recent years …

This paper presents the results of the premier shared task organized alongside the
Conference on Machine Translation (WMT) 2019. Participants were asked to build machine …

Despite the progress in machine translation quality estimation and evaluation in the last
years, decoding in neural machine translation (NMT) is mostly oblivious to this and centers …

The development of deep learning techniques has allowed Neural Machine Translation
(NMT) models to become extremely powerful, given sufficient training data and training time …

Recent studies have revealed a number of pathologies of neural machine translation (NMT)
systems. Hypotheses explaining these mostly suggest there is something fundamentally …

One of the difficulties of neural machine translation (NMT) is the recall and appropriate
translation of low-frequency words or phrases. In this paper, we propose a simple, fast, and …

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 …

In open-ended natural-language generation, existing text decoding methods typically
struggle to produce text which is both diverse and high-quality. Greedy and beam search are …

Minimum Bayes Risk (MBR) decoding is a method for choosing the outputs of a machine
learning system based not on the output with the highest probability, but the output with the …

Accurately detecting dysfluencies in spoken language can help to improve the performance
of automatic speech and language processing components and support the development of …