Paper meta data correction forr 2022.acl-long.480, closes #2929.

data/xml/2022.acl.xml

@@ -7696,11 +7696,11 @@ in the Case of Unambiguous Gender</title>
     </paper>
     <paper id="480">
       <title>From Simultaneous to Streaming Machine Translation by Leveraging Streaming History</title>
-      <author><first>Javier</first><last>Iranzo Sanchez</last></author>
+      <author><first>Javier</first><last>Iranzo-Sánchez</last></author>
       <author><first>Jorge</first><last>Civera</last></author>
-      <author><first>Alfons</first><last>Juan-Císcar</last></author>
+      <author><first>Alfons</first><last>Juan</last></author>
       <pages>6972-6985</pages>
-      <abstract>Simultaneous machine translation has recently gained traction thanks to significant quality improvements and the advent of streaming applications. Simultaneous translation systems need to find a trade-off between translation quality and response time, and with this purpose multiple latency measures have been proposed. However, latency evaluations for simultaneous translation are estimated at the sentence level, not taking into account the sequential nature of a streaming scenario. Indeed, these sentence-level latency measures are not well suited for continuous stream translation, resulting in figures that are not coherent with the simultaneous translation policy of the system being assessed. This work proposes a stream-level adaptation of the current latency measures based on a re-segmentation approach applied to the output translation, that is successfully evaluated on streaming conditions for a reference IWSLT task</abstract>
+      <abstract>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 sentence-level 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.</abstract>
       <url hash="3089f909">2022.acl-long.480</url>
       <attachment type="software" hash="9007090a">2022.acl-long.480.software.zip</attachment>
       <bibkey>iranzo-sanchez-etal-2022-simultaneous</bibkey>