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Revisiting Checkpoint Averaging for Neural Machine Translation 2

Revisiting Checkpoint Averaging for Neural Machine Translati...

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2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural language Processing, AACL-IJCNLP 2022

作者： Gao, Yingbo Herold, Christian Yang, Zijian Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department Rwth Aachen University AachenD-52056 Germany

ISBN: (纸本)9781959429043

Checkpoint averaging is a simple and effectivemethod to boost the performance of convergedneural machine translation models. The calculation is cheap to perform and the fact thatthe translation improvement almost comes forfree, makes it widely adopted in neural machine translation research. Despite the popularity, the method itself simply takes the mean ofthe model parameters from several checkpoints,the selection of which is mostly based on empirical recipes without many justifications. In thiswork, we revisit the concept of checkpoint averaging and consider several extensions. Specifically, we experiment with ideas such as usingdifferent checkpoint selection strategies, calculating weighted average instead of simplemean, making use of gradient information andfine-tuning the interpolation weights on development data. Our results confirm the necessityof applying checkpoint averaging for optimalperformance, but also suggest that the landscape between the converged checkpoints israther flat and not much further improvementcompared to simple averaging is to be obtained. © AACL-IJCNLP *** rights reserved

关键词： Neural machine translation

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Adapting neural machine translation with parallel synthetic data 2

Adapting neural machine translation with parallel synthetic ...

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2nd Conference on Machine Translation, WMT 2017

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

ISBN: (纸本)9781945626968

Recent works have shown that the usage of a synthetic parallel corpus can be effectively exploited by a neural machine translation system. In this paper, we propose a new method for adapting a general neural machine translation system to a specific task, by exploiting synthetic data. The method consists in selecting, from a large monolingual pool of sentences in the source language, those instances that are more related to a given test set. Next, this selection is automatically translated and the general neural machine translation system is fine-tuned with these data. For evaluating the adaptation method, we first conducted experiments in two controlled domains, with common and well-studied corpora. Then, we evaluated our proposal on a real e-commerce task, yielding consistent improvements in terms of translation quality. © 2017 Association for Computational Linguistics

关键词： Neural machine translation

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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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Local system voting feature for machine translation system combination 10

Local system voting feature for machine translation system c...

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10th Workshop on Statistical Machine Translation, WMT 2015 at the 2015 Conference on Empirical Methods in Natural language Processing, EMNLP 2015

作者： Freitag, Markus Peter, Jan-Thorsten Peitz, Stephan Feng, Minwei Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department Rwth Aachen University AachenD-52056 Germany

ISBN: (纸本)9781941643327

In this paper, we enhance the traditional confusion network system combination approach with an additional model trained by a neural network. This work is motivated by the fact that the commonly used binary system voting models only assign each input system a global weight which is responsible for the global impact of each input system on all translations. This prevents individual systems with low system weights from having influence on the system combination output, although in some situations this could be helpful. Further, words which have only been seen by one or few systems rarely have a chance of being present in the combined output. We train a local system voting model by a neural network which is based on the words themselves and the combinatorial occurrences of the different system outputs. This gives system combination the option to prefer other systems at different word positions even for the same sentence. © EMNLP 2015. All rights reserved.

关键词： Natural language processing systems

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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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The RWTH Aachen University English-German and German-English Unsupervised Neural Machine Translation Systems for WMT 2018 3

The RWTH Aachen University English-German and German-English...

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3rd Conference on Machine Translation, WMT 2018 at the Conference on Empirical Methods in Natural language Processing, EMNLP 2018

作者： Graça, Miguel Kim, Yunsu Schamper, Julian Geng, Jiahui Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)9781948087810

This paper describes the unsupervised neural machine translation (NMT) systems of the RWTH Aachen University developed for the English ↔ German news translation task of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018). Our work is based on iterative back-translation using a shared encoder-decoder NMT model. We extensively compare different vocabulary types, word embedding initialization schemes and optimization methods for our model. We also investigate gating and weight normalization for the word embedding layer. ©2018 Association for Computational Linguistics

关键词： Neural machine translation

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The rwth aachen german-english machine translation system for WMT 2014 9

The rwth aachen german-english machine translation system fo...

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9th Workshop on Statistical Machine Translation, WMT 2014 at the 52nd Conference of the Associationfor Computational Linguistics, ACL 2014

作者： Peitz, Stephan Wuebker, Joern Freitag, Markus Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)9781941643174

This paper describes the statistical machine translation (SMT) systems developed at RWTH Aachen University for the German!English translation task of the ACL 2014 Eighth Workshop on Statistical Machine Translation (WMT 2014). Both hierarchical and phrase-based SMT systems are applied employing hierarchical phrase reordering and word class language models. For the phrase-based system, we run discriminative phrase training. In addition, we describe our preprocessing pipeline for German!English. © Proceedings of the Annual Meeting of the Association for Computational Linguistics 2014.

关键词： Computer aided language translation

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Diving Deep into Context-Aware Neural Machine Translation 5

Diving Deep into Context-Aware Neural Machine Translation

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5th Conference on Machine Translation, WMT 2020

作者： Huo, Jingjing Herold, Christian Gao, Yingbo Dahlmann, Leonard Khadivi, Shahram Ney, Hermann eBay Inc. Aachen Germany Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (纸本)9781948087810

Context-aware neural machine translation (NMT) is a promising direction to improve the translation quality by making use of the additional context, e.g., document-level translation, or having meta-information. Although there exist various architectures and analyses, the effectiveness of different context-aware NMT models is not well explored yet. This paper analyzes the performance of document-level NMT models on four diverse domains with a varied amount of parallel document-level bilingual data. We conduct a comprehensive set of experiments to investigate the impact of document-level NMT. We find that there is no single best approach to document-level NMT, but rather that different architectures come out on top on different tasks. Looking at task-specific problems, such as pronoun resolution or headline translation, we find improvements in the context-aware systems, even in cases where the corpus-level metrics like BLEU show no significant improvement. We also show that document-level back-translation significantly helps to compensate for the lack of document-level bi-texts. © 2020 Association for Computational Linguistics

关键词： Neural machine translation

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The RWTH Aachen Machine Translation System for WMT 2013 8

The RWTH Aachen Machine Translation System for WMT 2013

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8th Workshop on Statistical Machine Translation, WMT 2013

作者： Peitz, Stephan Mansour, Saab Peter, Jan-Thorsten Schmidt, Christoph Wuebker, Joern Huck, Matthias Freitag, Markus Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)9781937284572

This paper describes the statistical machine translation (SMT) systems developed at RWTH Aachen University for the translation task of the ACL 2013 Eighth Workshop on Statistical Machine Translation (WMT 2013). We participated in the evaluation campaign for the French-English and German-English language pairs in both translation directions. Both hierarchical and phrase-based SMT systems are applied. A number of different techniques are evaluated, including hierarchical phrase reordering, translation model interpolation, domain adaptation techniques, weighted phrase extraction, word class language model, continuous space language model and system combination. By application of these methods we achieve considerable improvements over the respective baseline systems. © 2013 Association for Computational Linguistics

关键词： Machine translation

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Document-Level language Models for Machine Translation 8

Document-Level Language Models for Machine Translation

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8th Conference on Machine Translation, WMT 2023

作者： Petrick, Frithjof Herold, Christian Petrushkov, Pavel Khadivi, Shahram Ney, Hermann eBay Inc. Aachen Germany Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (纸本)9798891760417

Despite the known limitations, most machine translation systems today still operate on the sentence-level. One reason for this is, that most parallel training data is only sentence-level aligned, without document-level meta information available. In this work, we set out to build context-aware translation systems utilizing document-level monolingual data instead. This can be achieved by combining any existing sentence-level translation model with a document-level language model. We improve existing approaches by leveraging recent advancements in model combination. Additionally, we propose novel weighting techniques that make the system combination more flexible and significantly reduce computational overhead. In a comprehensive evaluation on four diverse translation tasks, we show that our extensions improve document-targeted scores substantially and are also computationally more efficient. However, we also find that in most scenarios, back-translation gives even better results, at the cost of having to re-train the translation system. Finally, we explore language model fusion in the light of recent advancements in large language models. Our findings suggest that there might be strong potential in utilizing large language models via model combination. © 2023 Association for Computational Linguistics.

关键词： Machine translation

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