Publication

SIMULEVAL : An Evaluation Toolkit for Simultaneous Translation

Conference on Empirical Methods in Natural Language Processing (EMNLP)


Abstract

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.

Related Publications

All Publications

NeurIPS - December 5, 2021

Interpretable agent communication from scratch (with a generic visual processor emerging on the side)

Roberto Dessì, Eugene Kharitonov, Marco Baroni

Electronics (MDPI) Journal - November 4, 2021

Performance Evaluation of Offline Speech Recognition on Edge Devices

Santosh Gondi, Vineel Pratap

EMNLP Conference on Machine Translation (WMT) - October 1, 2020

BERGAMOT-LATTE Submissions for the WMT20 Quality Estimation Shared Task

Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Vishrav Chaudhary, Mark Fishel, Francisco Guzmán, Lucia Specia

Electronics (MDPI) Journal - November 10, 2021

Performance and Efficiency Evaluation of ASR Inference on the Edge

Santosh Gondi, Vineel Pratap

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookie Policy