6 citations · 11 across the 4 of their papers we have counts for
7 papers · 1 filter
The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translation
Jie He, Tao Wang, Deyi Xiong +1
Does neural machine translation yield translations that are congenial with common sense? In this paper, we present a test suite to evaluate the commonsense reasoning capability of…
BLEURT Has Universal Translations: An Analysis of Automatic Metrics by Minimum Risk Training
Yiming Yan, Tao Wang, Chengqi Zhao +3
Automatic metrics play a crucial role in machine translation. Despite the widespread use of n-gram-based metrics, there has been a recent surge in the development of pre-trained mo…
Improving speech translation by fusing speech and text
Wenbiao Yin, Zhicheng Liu, Chengqi Zhao +3
In speech translation, leveraging multimodal data to improve model performance and address limitations of individual modalities has shown significant effectiveness. In this paper,…
Secoco: Self-Correcting Encoding for Neural Machine Translation
Tao Wang, Chengqi Zhao, Mingxuan Wang +3
This paper presents Self-correcting Encoding (Secoco), a framework that effectively deals with input noise for robust neural machine translation by introducing self-correcting pred…
The Volctrans Neural Speech Translation System for IWSLT 2021
Chengqi Zhao, Zhicheng Liu, Jian Tong +6
This paper describes the systems submitted to IWSLT 2021 by the Volctrans team. We participate in the offline speech translation and text-to-text simultaneous translation tracks. F…
Autocorrect in the Process of Translation -- Multi-task Learning Improves Dialogue Machine Translation
Tao Wang, Chengqi Zhao, Mingxuan Wang +2
Automatic translation of dialogue texts is a much needed demand in many real life scenarios. However, the currently existing neural machine translation delivers unsatisfying result…