  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. Evaluating simultaneous translation models is more
complex than offline models because the latency is another factor to consider
in addition to translation quality. The research community, despite its growing
focus on novel modeling approaches to simultaneous translation, currently lacks
a universal evaluation procedure. Therefore, we present SimulEval, an
easy-to-use and general evaluation toolkit for both simultaneous text and
speech translation. A server-client scheme is introduced to create a
simultaneous translation scenario, where the server sends source input and
receives predictions for evaluation and the client executes customized
policies. Given a policy, it automatically performs simultaneous decoding and
collectively reports several popular latency metrics. We also adapt latency
metrics from text simultaneous translation to the speech task. Additionally,
SimulEval is equipped with a visualization interface to provide better
understanding of the simultaneous decoding process of a system. SimulEval has
already been extensively used for the IWSLT 2020 shared task on simultaneous
speech translation. Code will be released upon publication.

Computation and Language