Abstract Machine translation (MT) is a technique that leverages computers to translate
human languages automatically. Nowadays, neural machine translation (NMT) which …

Simultaneous translation, which translates sentences before they are finished, is useful in
many scenarios but is notoriously difficult due to word-order differences. While the …

Simultaneous machine translation begins to translate each source sentence before the
source speaker is finished speaking, with applications to live and streaming scenarios …

Simultaneous machine translation models start generating a target sequence before they
have encoded or read the source sequence. Recent approaches for this task either apply a …

Simultaneous text translation and end-to-end speech translation have recently made great
progress but little work has combined these tasks together. We investigate how to adapt …

Simultaneous translation is widely useful but remains challenging. Previous work falls into
two main categories:(a) fixed-latency policies such as Ma et al.(2019) and (b) adaptive …

Simultaneous translation on both text and speech focuses on a real-time and low-latency
scenario where the model starts translating before reading the complete source input …

Simultaneous machine translation consists in starting output generation before the entire
input sequence is available. Wait-k decoders offer a simple but efficient approach for this …

Balancing accuracy and latency is a great challenge for simultaneous translation. To
achieve high accuracy, the model usually needs to wait for more streaming text before …

Adaptive policies are better than fixed policies for simultaneous translation, since they can
flexibly balance the tradeoff between translation quality and latency based on the current …