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[EN] This paper describes the participation of the MLLP research group of the Universitat Politècnica de València in the WMT 2019 Similar Language Translation Shared Task. We have submitted systems for the Portuguese ↔ Spanish language pair, in both directions. We have submitted systems based on the Transformer architecture as well as an in development novel architecture which we have called 2D alternating RNN. We have carried out domain adaptation through fine-tuning. ; The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 761758 (X5gon); the Government of Spain's research project Multisub, ref. RTI2018-094879-B-I00 (MCIU/AEI/FEDER, EU) and the Generalitat Valenciana's predoctoral research scholarship ACIF/2017/055. ; Baquero-Arnal, P.; Iranzo-Sánchez, J.; Civera Saiz, J.; Juan, A. (2019). The MLLP-UPV Spanish-Portuguese and Portuguese-Spanish Machine Translation Systems for WMT19 Similar Language Translation Task. The Association for Computational Linguistics. 179-184. http://hdl.handle.net/10251/180621 ; S ; 179 ; 184

[EN] Massive Open Online Courses (MOOCs) and Open Educational Resources (OER) are rapidly growing, but are not usually offered in multiple languages due to the lack of cost-effective solutions to translate the different objects comprising them and particularly videos. However, current state-of-the-art automatic speech recognition (ASR) and machine translation (MT) techniques have reached a level of maturity which opens the possibility of producing multilingual video subtitles of publishable quality at low cost. This work summarizes authors' experience in exploring this possibility in two real-life case studies: a MOOC platform and a large video lecture repository. Apart from describing the systems, tools and integration components employed for such purpose, a comprehensive evaluation of the results achieved is provided in terms of quality and efficiency. More precisely, it is shown that draft multilingual subtitles produced by domainadapted ASR/MT systems reach a level of accuracy that make them worth post-editing, instead of generating them ex novo, saving approximately 25%-75% of the time. Finally, the results reported on user multilingual data consumption reflect that multilingual subtitles have had a very positive impact in our case studies boosting student enrolment, in the case of the MOOC platform, by 70% relative. ; The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 287755 (transLectures) and from the EU's ICT Policy Support Programme as part of the Competitiveness and Innovation Framework Programme under grant agreement no. 621030 (EMMA). Additionally, it is supported by the Spanish research project TIN2015-68326-R (MINECO/FEDER). ; Valor Miró, JD.; Baquero-Arnal, P.; Civera Saiz, J.; Turró Ribalta, C.; Juan, A. (2018). Multilingual videos for MOOCs and OER. Educational Technology & Society. 21(2):1-12. http://hdl.handle.net/10251/122577 ; S ; 1 ; 12 ; 21 ; 2

[EN] The cascade approach to Speech Translation (ST) is based on a pipeline that concatenates an Automatic Speech Recognition (ASR) system followed by a Machine Translation (MT) system. These systems are usually connected by a segmenter that splits the ASR output into, hopefully, semantically self-contained chunks to be fed into the MT system. This is specially challenging in the case of streaming ST, where latency requirements must also be taken into account. This work proposes novel segmentation models for streaming ST that incorporate not only textual, but also acoustic information to decide when the ASR output is split into a chunk. An extensive and thorough experimental setup is carried out on the Europarl-ST dataset to prove the contribution of acoustic information to the performance of the segmentation model in terms of BLEU score in a streaming ST scenario. Finally, comparative results with previous work also show the superiority of the segmentation models proposed in this work. ; The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 761758 (X5Gon); the Government of Spain's research project Multisub, ref. RTI2018- 094879-B-I00 (MCIU/AEI/FEDER,EU), the Generalitat Valenciana's research project Classroom Activity Recognition, ref. PROMETEO/2019/111., FPU scholarship FPU18/04135; and the Generalitat Valencianas predoctoral research scholarship ACIF/2017/055. The authors wish to thank the anonymous reviewers for their criticisms and suggestions. ; Iranzo-Sánchez, J.; Giménez Pastor, A.; Silvestre Cerdà, JA.; Baquero-Arnal, P.; Civera Saiz, J.; Juan, A. (2020). Direct Segmentation Models for Streaming Speech Translation. Association for Computational Linguistics. 2599-2611. http://hdl.handle.net/10251/177537 ; S ; 2599 ; 2611

ACL materials are Copyright © 1963-2021 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License. ; [EN] This paper describes the statistical machine translation system built by the MLLP research group of Universitat Politècnica de València for the German¿English news translation shared task of the EMNLP 2018 Third Conference on Machine Translation (WMT18). We used an ensemble of Transformer architecture¿based neural machine translation systems. To train our system under ¿constrained¿ conditions, we filtered the provided parallel data with a scoring technique using character-based language models, and we added parallel data based on synthetic source sentences generated from the provided monolingual corpora. ; The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 761758 (X5gon); the Spanish government's TIN2015-68326-R (MINECO/FEDER) research project MORE, university collaboration grant programme 2017-2018, and faculty training scholarship FPU13/06241; the Generalitat Valenciana's predoctoral research scholarship ACIF/2017/055; as well as the Universitat Politecnica de València's PAID-01-17 R&D support programme. ; Iranzo-Sánchez, J.; Baquero-Arnal, P.; Garcés Díaz-Munío, G.; Martínez-Villaronga, A.; Civera Saiz, J.; Juan, A. (2018). The MLLP-UPV German-English Machine Translation System for WMT18. Association for Computational Linguistics (ACL). 418-424. https://doi.org/10.18653/v1/W18-6414 ; S ; 418 ; 424

[EN] The cascade approach to Speech Translation (ST) is based on a pipeline that concatenates an Automatic Speech Recognition (ASR) system followed by a Machine Translation (MT) system. Nowadays, state-of-the-art ST systems are populated with deep neural networks that are conceived to work in an offline setup in which the audio input to be translated is fully available in advance. However, a streaming setup defines a completely different picture, in which an unbounded audio input gradually becomes available and at the same time the translation needs to be generated under real-time constraints. In this work, we present a state-of-the-art streaming ST system in which neural-based models integrated in the ASR and MT components are carefully adapted in terms of their training and decoding procedures in order to run under a streaming setup. In addition, a direct segmentation model that adapts the continuous ASR output to the capacity of simultaneous MT systems trained at the sentence level is introduced to guarantee low latency while preserving the translation quality of the complete ST system. The resulting ST system is thoroughly evaluated on the real-life streaming Europarl-ST benchmark to gauge the trade-off between quality and latency for each component individually as well as for the complete ST system. ; The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 761758 (X5Gon) and 952215 (TAILOR); the Government of Spain's research project Multisub, ref. RTI2018-094879-B-I00 (MCIU/AEI/FEDER,EU) and FPU scholarships FPU14/03981 and FPU18/04135; and the Generalitat Valenciana's research project Classroom Activity Recognition, ref. PROMETEO/2019/111 and predoctoral research scholarship ACIF/2017/055. ; Iranzo-Sánchez, J.; Jorge-Cano, J.; Baquero-Arnal, P.; Silvestre Cerdà, JA.; Giménez Pastor, A.; Civera Saiz, J.; Sanchis Navarro, JA. (2021). Streaming cascade-based speech translation leveraged by a direct ...