The HW - TSC Video Speech Translation System at IWSLT 2020 (2020)

Abstract: The paper presents details of our system in the IWSLT Video Speech Translation evaluation. The system works in a cascade form, which contains three modules: 1) A proprietary ASR system. 2) A disfluency correction system aims to remove interregnums or other disfluent expressions with a fine-tuned BERT and a series of rule-based algorithms. 3) An NMT System based on the Transformer and trained with massive publicly available corpus.

## introduction there has been great advances in neural machine translations (nmt) in recent years which also promotes multimodal translation including image @xcite , speech and video translation @xcite @xcite . for speech and video translation, there are basically two types of systems (i.e. cascade and end-to-end). cascade systems are often composed of several independent modules, where each one can be trained with intermediate inputs and labels. end-to-end systems can be fully differentiable and trained with original multimodality data. in the iwslt 2020 video speech translation task @xcite , participants are required to develop systems to translate speeches in the video from source language into target language. however, we consider that visual contexts are not necessarily important for a translation task, therefore we only use audio as contexts, and treat it as a offline speech translation task, in addition, our system is built mainly for chinese to english translation. we choose to build our system in a cascade manner because training an end-to-end system requires massive aligned audio and text data, which is hard to find. on the other hand, a cascade system allows us to train each part separately, which is more feasible. our system is composed with three modules. 1) a proprietary asr system. 2) a disfluency correction system @xcite . 3) an nmt model based on the transformer @xcite . ## conclusion in this paper, we introduce our system for iwslt 2020 video speech translation evaluation. our cascade system is developed and evaluated separately, we select the best strategy for each module to integrate a pipeline system which finally makes predictions for our submission.