Álvaro Peris

Also published as: Alvaro Peris


2020

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A User Study of the Incremental Learning in NMT
Miguel Domingo | Mercedes García-Martínez | Álvaro Peris | Alexandre Helle | Amando Estela | Laurent Bié | Francisco Casacuberta | Manuel Herranz
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation

In the translation industry, human experts usually supervise and post-edit machine translation hypotheses. Adaptive neural machine translation systems, able to incrementally update the underlying models under an online learning regime, have been proven to be useful to improve the efficiency of this workflow. However, this incremental adaptation is somewhat unstable, and it may lead to undesirable side effects. One of them is the sporadic appearance of made-up words, as a byproduct of an erroneous application of subword segmentation techniques. In this work, we extend previous studies on on-the-fly adaptation of neural machine translation systems. We perform a user study involving professional, experienced post-editors, delving deeper on the aforementioned problems. Results show that adaptive systems were able to learn how to generate the correct translation for task-specific terms, resulting in an improvement of the user’s productivity. We also observed a close similitude, in terms of morphology, between made-up words and the words that were expected.

2019

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Demonstration of a Neural Machine Translation System with Online Learning for Translators
Miguel Domingo | Mercedes García-Martínez | Amando Estela Pastor | Laurent Bié | Alexander Helle | Álvaro Peris | Francisco Casacuberta | Manuel Herranz Pérez
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

We present a demonstration of our system, which implements online learning for neural machine translation in a production environment. These techniques allow the system to continuously learn from the corrections provided by the translators. We implemented an end-to-end platform integrating our machine translation servers to one of the most common user interfaces for professional translators: SDL Trados Studio. We pretend to save post-editing effort as the machine is continuously learning from its mistakes and adapting the models to a specific domain or user style.

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A Neural, Interactive-predictive System for Multimodal Sequence to Sequence Tasks
Álvaro Peris | Francisco Casacuberta
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

We present a demonstration of a neural interactive-predictive system for tackling multimodal sequence to sequence tasks. The system generates text predictions to different sequence to sequence tasks: machine translation, image and video captioning. These predictions are revised by a human agent, who introduces corrections in the form of characters. The system reacts to each correction, providing alternative hypotheses, compelling with the feedback provided by the user. The final objective is to reduce the human effort required during this correction process. This system is implemented following a client-server architecture. For accessing the system, we developed a website, which communicates with the neural model, hosted in a local server. From this website, the different tasks can be tackled following the interactive–predictive framework. We open-source all the code developed for building this system. The demonstration in hosted in http://casmacat.prhlt.upv.es/interactive-seq2seq.

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Incremental Adaptation of NMT for Professional Post-editors: A User Study
Miguel Domingo | Mercedes García-Martínez | Álvaro Peris | Alexandre Helle | Amando Estela | Laurent Bié | Francisco Casacuberta | Manuel Herranz
Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks

2018

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Active Learning for Interactive Neural Machine Translation of Data Streams
Álvaro Peris | Francisco Casacuberta
Proceedings of the 22nd Conference on Computational Natural Language Learning

We study the application of active learning techniques to the translation of unbounded data streams via interactive neural machine translation. The main idea is to select, from an unbounded stream of source sentences, those worth to be supervised by a human agent. The user will interactively translate those samples. Once validated, these data is useful for adapting the neural machine translation model. We propose two novel methods for selecting the samples to be validated. We exploit the information from the attention mechanism of a neural machine translation system. Our experiments show that the inclusion of active learning techniques into this pipeline allows to reduce the effort required during the process, while increasing the quality of the translation system. Moreover, it enables to balance the human effort required for achieving a certain translation quality. Moreover, our neural system outperforms classical approaches by a large margin.

2017

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Adapting Neural Machine Translation with Parallel Synthetic Data
Mara Chinea-Ríos | Álvaro Peris | Francisco Casacuberta
Proceedings of the Second Conference on Machine Translation

2016

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Interactive-Predictive Translation Based on Multiple Word-Segments
Miguel Domingo | Alvaro Peris | Francisco Casacuberta
Proceedings of the 19th Annual Conference of the European Association for Machine Translation