Information-Transport-based Policy for Simultaneous Translation
Shaolei Zhang 1,2, Yang Feng 1,2 1Key Laboratory of Intelligent Information Processing Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS) 2 University of Chinese Academy of Sciences, Beijing, China {zhangshaolei20z, Corresponding author: Yang Feng.
Abstract
Simultaneous translation (ST) outputs translation while receiving the source inputs, and hence requires a policy to determine whether to translate a target token or wait for the next source token. The major challenge of ST is that each target token can only be translated based on the current received source tokens, where the received source information will directly affect the translation quality. So naturally, how much source information is received for the translation of the current target token is supposed to be the pivotal evidence for the ST policy to decide between translating and waiting. In this paper, we treat the translation as information transport from source to target and accordingly propose an Information-Transport-based Simultaneous Translation (ITST). ITST quantifies the transported information weight from each source token to the current target token, and then decides whether to translate the target token according to its accumulated received information. Experiments on both text-to-text ST and speech-to-text ST (a.k.a., streaming speech translation) tasks show that ITST outperforms strong baselines and achieves state-of-the-art performance111Code is available at h
中文速览
同声传译（Simultaneous Translation, ST）需要一边接收源语言输入、一边实时输出译文，核心难题在于：翻译每个目标词时只能用到已收到的源词，如何判断"收到的信息够不够翻译当前词"是决定读还是写的关键，而以往方法往往缺乏对这一信息量的明确度量。作者将翻译过程类比为"信息从源端向目标端的搬运"，借鉴最优传输（Optimal Transport）思想，提出了基于信息传输的同声传译方法（ITST），通过可学习的传输权重矩阵量化每个源词向当前目标词贡献了多少信息，并在积累信息超过设定阈值后才触发翻译，同时引入对角形式的延迟代价矩阵约束传输行为、用课程学习策略从易到难训练出一个可应对任意延迟档位的统一模型。在英越、德英文本同传以及流式语音翻译等多项任务上，ITST 均超越了此前的强基线，达到当时最优水平，证明将翻译决策建立在可解释的信息量化基础上是一条切实可行且有效的路径。
原文 arXiv:2210.12357;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2210.12357v2