Simultaneous Machine Translation is the task of incrementally translating an input sentence before it is fully available . Currently , simultaneous translation is carried out by translating each sentence independently of the previously translated text . More generally , Streaming MT can be understood as an extension of Simultaneous MT to the incremental translation of a continuous input text stream . In this work , a state-of-the-art simultaneous sentencelevel MT system is extended to the streaming setup by leveraging the streaming history . Extensive empirical results are reported on IWSLT Translation Tasks , showing that leveraging the streaming history leads to significant quality gains . In particular , the proposed system proves to compare favorably to the best performing systems . Simultaneous Machine Translation ( MT ) is the task of incrementally translating an input sentence before it is fully available . Indeed , simultaneous MT can be naturally understood in the scenario of translating a text stream as a result of an upstream Automatic Speech Recognition ( ASR ) process . This setup defines a simultaneous Speech Translation ( ST ) scenario that is gaining momentum due to the vast number of industry applications that could be exploited based on this technology , from person-toperson communication to subtitling of audiovisual content , just to mention two main applications . These real-world streaming applications motivate us to move from simultaneous to streaming MT , understanding streaming MT as the task of simultaneously translating a potentially unbounded and unsegmented text stream . Streaming MT poses two main additional challenges over simultaneous MT . First , the MT system must be able to leverage the streaming history beyond the sentence level both at training and inference time . Second , the system must work under latency constraints over the entire stream . With regard to exploiting streaming history , or more generally sentence context , it is worth mentioning the significant amount of previous work in offline MT at sentence level ( Tiedemann and Scherrer , 2017 ; Agrawal et al . , 2018 ) , document level ( Scherrer et al . , 2019 ; Ma et al . , 2020a ; Zheng et al . , 2020b ; Li et al . , 2020 ; Maruf et al . , 2021 ; Zhang et al . , 2021 ) , and in related areas such as language modelling ( Dai et al . , 2019 ) that has proved to lead to quality gains . Also , as reported in ( Li et al . , 2020 ) , more robust ST systems can be trained by taking advantage of the context across sentence boundaries using a data augmentation strategy similar to the prefix training methods proposed in ( Niehues et al . , 2018 ; Ma et al . , 2019 ) . This data augmentation strategy was suspected to boost re-translation performance when compared to conventional simultaneous MT systems ( Arivazhagan et al . , 2020 ) . Nonetheless , with the notable exception of ( Schneider and Waibel , 2020 ) , sentences in simultaneous MT are still translated independently from each other ignoring the streaming history . ( Schneider and Waibel , 2020 ) proposed an end-to-end streaming MT model with a Transformer architecture based on an Adaptive Computation Time method with a monotonic encoderdecoder attention . This model successfully uses the streaming history and a relative attention mechanism inspired by Transformer-XL ( Dai et al . , 2019 ) . Indeed , this is an MT model that sequentially translates the input stream without the need for a segmentation model . However , it is hard to interpret the latency of their streaming MT model because the authors observe that the current sentence-level latency measures , Average Proportion ( AP ) ( Cho and Esipova , 2016 ) , Average Lagging ( AL ) ( Ma et al . , 2019 ) and Differentiable Average Lagging ( DAL ) ( Cherry and Foster , 2019 ) do not perform well on a streaming setup . This fact is closely related to the second challenge mentioned above , which is that the system must work under latency constraints over the entire stream . Indeed , current sentence-level latency measures do not allow us to appropriately gauge the latency of streaming MT systems . To this purpose , ( Iranzo-Sánchez et al . , 2021 ) recently proposed a stream-level adaptation of the sentence-level latency measures based on the conventional re-segmentation approach applied to the ST output in order to evaluate translation quality ( Matusov et al . , 2005 ) . In this work , the simultaneous MT model based on a unidirectional encoder-decoder and training along multiple wait-k paths proposed by ( Elbayad et al . , 2020a ) is evolved into a streamingready simultaneous MT model . To achieve this , model training is performed following a sentenceboundary sliding-window strategy over the parallel stream that exploits the idea of prefix training , while inference is carried out in a single forward pass on the source stream that is segmented by a Direct Segmentation ( DS ) model ( Iranzo-Sánchez et al . , 2020 ) . In addition , a refinement of the unidirectional encoder-decoder that takes advantage of longer context for encoding the initial positions of the streaming MT process is proposed . This streaming MT system is thoroughly assessed on IWSLT translation tasks to show how leveraging the streaming history provides systematic and significant BLEU improvements over the baseline , while reported stream-adapted latency measures are fully consistent and interpretable . Finally , our system favourably compares in terms of translation quality and latency to the latest state-of-the-art simultaneous MT systems ( Ansari et al . , 2020 ) . This paper is organized as follows . Next section provides a formal framework for streaming MT to accommodate streaming history in simultaneous MT . Section 3 presents the streaming experimental setup whose results are reported and discussed in Section 4 . Finally , conclusions and future work are drawn in Section 5 . In this work , a formalization of streaming MT as a generalization of simultaneous MT has been proposed in order to define a theoretical framework in which our two contributions have been made . On the one hand , we successfully leverage streaming history across sentence boundaries for a simultaneous MT system based on multiple wait-k paths that allows our system to greatly improve the results of the sentence-level baseline . On the other hand , our PBE is able to take into account longer context information than its unidirectional counterpart , while keeping the same training efficiency . Our proposed MT system has been evaluated under a realistic streaming setting being able to reach similar translation quality than a state-of-theart segmentation-free streaming MT system at a fraction of its latency . Additionally , our system has been shown to be competitive when compared with state-of-the-art simultaneous MT systems optimized for sentence-level translation , obtaining excellent results using a single model across a wide range of latency levels , thanks to its flexible inference policy . In terms of future work , additional training and inference procedures that take advantage of the streaming history in streaming MT are still open for research . One important avenue of improvement is to devise more robust training methods , so that simultaneous models can perform as well as their offline counterparts when carrying out inference at higher latencies . The segmentation model , though proved useful in a streaming setup , adds complexity and can greatly affect translation quality . Thus , the development of segmentation-free streaming MT models is another interesting research topic .

Challenge: The streaming machine translation task posts additional challenges over simultaneous counterparts which are the leverage of the streaming history beyond sentence level and latency constraints.
Approach: They extend a unidirectional encoder-decoder-based simultaneous sentence-level system to the streaming setup by leveraging the streaming history across sentence boundaries.
Outcome: The proposed model outperforms the baseline model and is comparable to the state-of-the-art simultaneous systems in quality and latency on IWSLT translation tasks.