Information-Transport-based Policy for Simultaneous Translation (2022)

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 performance.

## introduction simultaneous translation (st) @xcite @xcite , which outputs translation while receiving the streaming inputs, is essential for many realtime scenarios, such as simultaneous interpretation, online subtitles and live broadcasting. compared with the conventional full-sentence machine translation (mt) @xcite , st additionally requires a read/write policy to decide whether to wait for the next source input (a.k.a., read) or generate a target token (a.k.a., write). the goal of st is to achieve high-quality translation under low latency, however, the major challenge is that the low-latency requirement restricts the st model to translating each target token only based on current received source tokens @xcite . to mitigate the impact of this restriction on translation quality, st needs a reasonable read/write policy to ensure that before translating, the received source information is sufficient to generate the current target token @xcite . to achieve this, read/write policy should measure the amount of received source information, if the received source information is sufficient for translation, the model translates a target token, otherwise the model waits for the next input. however, previous read/write policies, involving fixed and adaptive, often lack an explicit measure of how much source information is received for the translation. fixed policy decides read/write according to predefined rules @xcite and sometimes forces the model to start translating even though the received source information is insufficient, thereby affecting the translation quality. adaptive policy can dynamically adjust read/write @xcite to achieve better performance. however, previous adaptive policies often directly predict a variable based on the inputs to indicate read/write decision @xcite @xcite , without explicitly modeling the amount of information that the received source tokens provide to the currently generated target token. under these grounds, we aim to develop a reasonable read/write policy that takes the received source information as evidence for read/write. for the st process, source tokens provide information, while target tokens receive information and then perform translating, thereby the translation process can be treated as information transport from source to target. along this line, if we are well aware of how much information is transported from each source token to the target token, then it is natural to grasp the total information provided by the received source tokens for the current target token, thereby ensuring that the source information is sufficient for translation. to this end, we propose information-transportbased simultaneous translation (itst). borrowing the idea from the optimal transport problem @xcite , itst explicitly quantifies the transported information weight from each source token to the current target token during translation. then, itst starts translating after judging that the amount of information provided by received source tokens for the current target token has reached a sufficient proportion. as shown in the schematic diagram in figure 1 , assuming that 70% source information is sufficient for translation, itst first quantifies the transport information weight from each source token to the current target token (e.g., 0.15, 0.28, • • • ). with the first three source tokens, the accumulated received information is 45%, less than 70%, then itst selects read. after receiving the fourth source token, the accumulated information received by the current target token becomes 78%, thus itst selects write to translate the current target token. experiments on both textto-text and speech-to-text simultaneous translation tasks show that itst outperforms strong baselines and achieves state-of-the-art performance. ## the proposed method we propose information-transport-based simultaneous translation (itst) to explicitly measure the source information projected to the current generated target token. during the st process, itst models the information transport to grasp how much information is transported from each source token to the current target token (sec.3.1). then, itst starts translating a target token after its accumulated received information is sufficient (sec.3.2). details of itst are as follows. ## analysis we conduct extensive analyses to study the specific improvement of itst. unless otherwise specified, all results are reported on @xmath0. 6 ). 'w/o l norm ': remove regular term for normalization in eq.( 10 ). ## conclusion in this paper, we treat the translation as the information transport from source to target and accordingly propose information-transport-based policy (itst). experiments on both text-to-text and speech-to-text st tasks show the superiority of itst in terms of performance, training method and policy quality.