Javier Iranzo-Sánchez


2020

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Direct Segmentation Models for Streaming Speech Translation
Javier Iranzo-Sánchez | Adrià Giménez Pastor | Joan Albert Silvestre-Cerdà | Pau Baquero-Arnal | Jorge Civera Saiz | Alfons Juan
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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 throughly 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.

2019

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The MLLP-UPV Supervised Machine Translation Systems for WMT19 News Translation Task
Javier Iranzo-Sánchez | Gonçal Garcés Díaz-Munío | Jorge Civera | Alfons Juan
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

This paper describes the participation of the MLLP research group of the Universitat Politècnica de València in the WMT 2019 News Translation Shared Task. In this edition, we have submitted systems for the German ↔ English and German ↔ French language pairs, participating in both directions of each pair. Our submitted systems, based on the Transformer architecture, make ample use of data filtering, synthetic data and domain adaptation through fine-tuning.

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The MLLP-UPV Spanish-Portuguese and Portuguese-Spanish Machine Translation Systems for WMT19 Similar Language Translation Task
Pau Baquero-Arnal | Javier Iranzo-Sánchez | Jorge Civera | Alfons Juan
Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)

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.

2018

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The MLLP-UPV German-English Machine Translation System for WMT18
Javier Iranzo-Sánchez | Pau Baquero-Arnal | Gonçal V. Garcés Díaz-Munío | Adrià Martínez-Villaronga | Jorge Civera | Alfons Juan
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

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.