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Are automatic metrics robust and reliable in specific machine translation tasks? 21

Are automatic metrics robust and reliable in specific machin...

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21st Annual Conference of the European Association for Machine Translation, EAMT 2018

作者： Chinea-Rios, Mara Peris, Álvaro Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain

ISBN: (纸本)9788409019014

We present a comparison of automatic metrics against human evaluations of translation quality in several scenarios which were unexplored up to now. Our experimentation was conducted on translation hypotheses that were problematic for the automatic metrics, as the results greatly diverged from one metric to another. We also compared three different translation technologies. Our evaluation shows that in most cases, the metrics capture the human criteria. However, we face failures of the automatic metrics when applied to some domains and systems. Interestingly, we find that automatic metrics applied to the neural machine translation hypotheses provide the most reliable results. Finally, we provide some advice when dealing with these problematic domains. © 2018 The authors. This article is licensed under a Creative Commons 3.0 licence, no derivative works, attribution, CC-BY-ND.

关键词： Neural machine translation

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Active learning for interactive neural machine translation of data streams 22

Active learning for interactive neural machine translation o...

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22nd Conference on Computational Natural language Learning, CoNLL 2018

作者： Peris, Álvaro Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain

ISBN: (纸本)9781948087728

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. © 2018 Association for Computational Linguistics.

关键词： Neural machine translation

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Limitations and Challenges of Unsupervised Cross-lingual Pre-training 15

Limitations and Challenges of Unsupervised Cross-lingual Pre...

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15th Conference of the Association for Machine Translation in the Americas, AMTA 2022

作者： Zaragoza, Martín Quesada Casacuberta, Francisco Research Center of Pattern Recognition and Human Language Technology Universitat Politècnica de València Valencia46022 Spain

Cross-lingual alignment methods for monolingual language representations have received notable attention in recent years. However, their use in machine translation pre-training remains scarce. This work tries to shed light on the effects of some of the factors that play a role in cross-lingual pre-training, both for cross-lingual mappings and their integration in supervised neural models. The results show that unsupervised cross-lingual methods are effective at inducing alignment even for distant languages and they benefit noticeably from subword information. However, we find that their effectiveness as pre-training models in machine translation is severely limited due to their cross-lingual signal being easily distorted by the principal network during training. Moreover, the learned bilingual projection is too restrictive to allow said network to learn properly when the embedding weights are frozen. © AMTA 2022 - 15th Conference of the Association for Machine Translation in the Americas, Proceedings.

关键词： Machine translation

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Comparison of Conventional Hybrid and CTC/Attention Decoders for Continuous Visual Speech recognition 30

Comparison of Conventional Hybrid and CTC/Attention Decoders...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on language Resources and Evaluation, LREC-COLING 2024

作者： Gimeno-Gómez, David Martínez-Hinarejos, Carlos D. Pattern Recognition and Human Language Technologies Research Center Universitat Politècnica de València Camino de Vera s/n València46022 Spain

ISBN: (纸本)9782493814104

Thanks to the rise of deep learning and the availability of large-scale audio-visual databases, recent advances have been achieved in Visual Speech recognition (VSR). Similar to other speech processing tasks, these end-to-end VSR systems are usually based on encoder-decoder architectures. While encoders are somewhat general, multiple decoding approaches have been explored, such as the conventional hybrid model based on Deep Neural Networks combined with Hidden Markov Models (DNN-HMM) or the Connectionist Temporal Classification (CTC) paradigm. However, there are languages and tasks in which data is scarce, and in this situation, there is not a clear comparison between different types of decoders. Therefore, we focused our study on how the conventional DNN-HMM decoder and its state-of-the-art CTC/Attention counterpart behave depending on the amount of data used for their estimation. We also analyzed to what extent our visual speech features were able to adapt to scenarios for which they were not explicitly trained, either considering a similar dataset or another collected for a different language. Results showed that the conventional paradigm reached recognition rates that improve the CTC/Attention model in data-scarcity scenarios along with a reduced training time and fewer parameters. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： Hidden Markov models

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Online optimisation of log-linear weights in interactive machine translation 9

Online optimisation of log-linear weights in interactive mac...

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9th International Conference on language Resources and Evaluation, LREC 2014

作者： Rios, Mara Chinea Sanchis-Trilles, Germán Ortiz-Martínez, Daniel Casacuberta, Francisco Pattern Recognition and Human Language Technologies Group Universitat Politècnica de València Camino de Vera s/n Valencia Spain

ISBN: (纸本)9782951740884

Whenever the quality provided by a machine translation system is not enough, a human expert is required to correct the sentences provided by the machine translation system. In such a setup, it is crucial that the system is able to learn from the errors that have already been corrected. In this paper, we analyse the applicability of discriminative ridge regression for learning the log-linear weights of a state-of-the - art machine translation system underlying an interactive machine translation framework, with encouraging results.

关键词： Machine translation

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Online learning of log-linear weights in interactive machine translation

Online learning of log-linear weights in interactive machine...

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Joint 7th Spanish Speech Technology Workshop and the Iberian SLTech Workshop, IberSPEECH 2012

作者： López-Salcedo, Francisco-Javier Sanchis-Trilles, Germán Casacuberta, Francisco Pattern Recognition and Human Language Technologies Group Departamento de Sistemas Informáticos Y Computación Universitat Politècnica de València Spain

ISBN: (纸本)9783642352911

Whenever the quality provided by a machine translation system is not enough, a human expert is required to correct the sentences provided by the machine translation system. In this environment, the human translator is generating bilingual data after each translation has been marked as correct, and expects the system to be able to learn from the errors made. In this paper, we analyse the appropriateness of discriminative ridge regression for adapting the scaling factors of a state-of-the-art machine translation system within a conventional post-editing scenario and also within an interactive machine translation setup. Results show that the strategies applied in the former setup cannot be directly applied in the latter framework. Hence, the discriminative ridge regression is revised and adapted for the interactive machine translation framework, with encouraging results. © 2012 Springer-Verlag.

关键词： Machine translation

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A multimodal crowdsourcing framework for transcribing historical handwritten documents 16

A multimodal crowdsourcing framework for transcribing histor...

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16th ACM Symposium on Document Engineering, DocEng 2016

作者： Granell, Emilio Martínez-Hinarejos, Carlos D. Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Camino Vera s/n 46022 Valencia Spain

ISBN: (纸本)9781450344388

Transcription of handwritten historical documents is one of the main topics in document analysis systems, due to cultural reasons. State-of-the-art handwritten text recognition systems allow to speed up the transcription task. Currently, this automatic transcription is far from perfect, and human expert revision is required in order to obtain the actual transcription. In this context, crowdsourcing emerged as a powerful tool for massive transcription at a relatively low cost, since the supervision effort of professional transcribers may be dramatically reduced. However, current transcription crowdsourcing platforms are mainly limited to the use of nonmobile devices, since the use of keyboards in mobile devices is not friendly enough for most users. This work presents the alternative of using speech dictation of handwritten text lines as transcription source in a crowdsourcing platform. The experiments explore how an initial handwritten text recognition hypothesis can be improved by using the contribution of speech recognition from several speakers, providing as a final result a better hypothesis to be amended by a professional transcriber with less effort.

关键词： Speech recognition

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Creating the best development corpus for Statistical Machine Translation systems 21

Creating the best development corpus for Statistical Machine...

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21st Annual Conference of the European Association for Machine Translation, EAMT 2018

作者： Chinea-Rios, Mara Sanchis-Trilles, Germán Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain Sciling València Spain

ISBN: (纸本)9788409019014

We propose and study three different novel approaches for tackling the problem of development set selection in Statistical Machine Translation. We focus on a scenario where a machine translation system is leveraged for translating a specific test set, without further data from the domain at hand. Such test set stems from a real application of machine translation, where the texts of a specific e-commerce were to be translated. For developing our development-set selection techniques, we first conducted experiments in a controlled scenario, where labelled data from different domains was available, and evaluated the techniques both with classification and translation quality metrics. Then, the best-performing techniques were evaluated on the e-commerce data at hand, yielding consistent improvements across two language directions. © 2018 The authors. This article is licensed under a Creative Commons 3.0 licence, no derivative works, attribution, CC-BY-ND.

关键词： Machine translation

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Impact of automatic segmentation on the quality, productivity and self-reported post-editing effort of intralingual subtitles 10

Impact of automatic segmentation on the quality, productivit...

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10th International Conference on language Resources and Evaluation, LREC 2016

作者： Álvarez, Aitor Balenciaga, Marina Del Pozo, Arantza Arzelus, Haritz Matamala, Anna Martínez-Hinarejos, Carlos-D. Human Speech and Language Technology Group Vicomtech-IK4 San-Sebastian Spain Department of Translation Interpreting and East Asian Studies UAB Barcelona Spain Pattern Recognition and Human Language Technologies Research Center Universitat Politècnica de València Spain

ISBN: (纸本)9782951740891

This paper describes the evaluation methodology followed to measure the impact of using a machine learning algorithm to automatically segment intralingual subtitles. The segmentation quality, productivity and self-reported post-editing effort achieved with such approach are shown to improve those obtained by the technique based in counting characters, mainly employed for automatic subtitle segmentation currently. The corpus used to train and test the proposed automated segmentation method is also described and shared with the community, in order to foster further research in this area.

关键词： Productivity

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Bi-modal handwritten text recognition (BiHTR) ICPR 2010 contest report

Bi-modal handwritten text recognition (BiHTR) ICPR 2010 cont...

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20th International Conference on pattern recognition, ICPR 2010

作者： Pastor, Moisés Paredes, Roberto Pattern Recognition and Human Language Technologies group Department of Information Systems and Computation Technical University of Valencia Cami de Vera s/n. Valencia46022 Spain

ISBN: (纸本)9783642177101

Handwritten text is generally captured through two main modalities: off-line and on-line. Each modality has advantages and disadvantages, but it seems clear that smart approaches to handwritten text recognition (HTR) should make use of both modalities in order to take advantage of the positive aspects of each one. A particularly interesting case where the need of this bi-modal processing arises is when an off-line text, written by some writer, is considered along with the on-line modality of the same text written by another writer. This happens, for example, in computer-assisted transcription of old documents, where on-line text can be used to interactively correct errors made by a main off-line HTR system. In order to develop adequate techniques to deal with this challenging bi-modal HTR recognition task, a suitable corpus is needed. We have collected such a corpus using data (word segments) from the publicly available off-line and on-line IAM data sets. In order to provide the Community with an useful corpus to make easy tests, and to establish baseline performance figures, we have proposed this handwritten bi-modal contest. Here is reported the results of the contest with two participants, one of them achieved a 0% classification error rate, whilst the other participant achieved an interesting 1.5%. © 2010, Springer-Verlag Berlin Heidelberg.

关键词： Character recognition

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