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检索条件"机构=Human Language Technology and Pattern Recognition Group Computer Science Department"

共 238 条记录，以下是21-30 订阅

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Insertion and deletion models for statistical machine translation

Insertion and deletion models for statistical machine transl...

引用

2012 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL HLT 2012

作者： Huck, Matthias Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)1937284204

We investigate insertion and deletion models for hierarchical phrase-based statistical machine translation. Insertion and deletion models are designed as a means to avoid the omission of content words in the hypotheses. In our case, they are implemented as phrase-level feature functions which count the number of inserted or deleted words. An English word is considered inserted or deleted based on lexical probabilities with the words on the foreign language side of the phrase. Related techniques have been employed before by Och et al. (2003) in an n-best reranking framework and by Mauser et al. (2006) and Zens (2008) in a standard phrase-based translation system. We propose novel thresholding methods in this work and study insertion and deletion features which are based on two different types of lexicon models. We give an extensive experimental evaluation of all these variants on the NIST Chinese → English translation task. © 2012 Association for Computational Linguistics.

关键词： computer aided language translation

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On the Alignment Problem in Multi-Head Attention-Based Neural Machine Translation 3

On the Alignment Problem in Multi-Head Attention-Based Neura...

引用

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

作者： Alkhouli, Tamer Bretschner, Gabriel Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)9781948087810

This work investigates the alignment problem in state-of-the-art multi-head attention models based on the transformer architecture. We demonstrate that alignment extraction in transformer models can be improved by augmenting an additional alignment head to the multi-head source-to-target attention component. This is used to compute sharper attention weights. We describe how to use the alignment head to achieve competitive performance. To study the effect of adding the alignment head, we simulate a dictionary-guided translation task, where the user wants to guide translation using pre-defined dictionary entries. Using the proposed approach, we achieve up to 3.8% BLEU improvement when using the dictionary, in comparison to 2.4% BLEU in the baseline case. We also propose alignment pruning to speed up decoding in alignment-based neural machine translation (ANMT), which speeds up translation by a factor of 1.8 without loss in translation performance. We carry out experiments on the shared WMT 2016 English→Romanian news task and the BOLT Chinese→English discussion forum task. © 2018 Association for Computational Linguistics.

关键词： Alignment

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The RWTH Aachen University English-German and German-English machine translation system for WMT 2017 2

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

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2nd Conference on Machine Translation, WMT 2017

作者： Peter, Jan-Thorsten Guta, Andreas Alkhouli, Tamer Bahar, Parnia Rosendahl, Jan Rossenbach, Nick Graça, Miguel Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)9781945626968

This paper describes the statistical machine translation system developed at RWTH Aachen University for the English?German and German?English translation tasks of the EMNLP 2017 Second Conference on Machine Translation (WMT 2017). We use ensembles of attention-based neural machine translation system for both directions. We use the provided parallel and synthetic data to train the models. In addition, we also create a phrasal system using joint translation and reordering models in decoding and neural models in rescoring. © 2017 Association for Computational Linguistics

关键词： Neural machine translation

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The RWTH Aachen University Filtering System for the WMT 2018 Parallel Corpus Filtering Task 3

The RWTH Aachen University Filtering System for the WMT 2018...

引用

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

作者： Rossenbach, Nick Rosendahl, Jan Kim, Yunsu Graça, Miguel Gokrani, Aman 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 submission of RWTH Aachen University for the De→En parallel corpus filtering task of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018). We use several rule-based, heuristic methods to preselect sentence pairs. These sentence pairs are scored with count-based and neural systems as language and translation models. In addition to single sentence-pair scoring, we further implement a simple redundancy removing heuristic. Our best performing corpus filtering system relies on recurrent neural language models and translation models based on the transformer architecture. A model trained on 10M randomly sampled tokens reaches a performance of 9.2% BLEU on newstest2018. Using our filtering and ranking techniques we achieve 34.8% BLEU. ©2018 Association for Computational Linguistics

关键词： Heuristic methods

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Unifying Input and Output Smoothing in Neural Machine Translation 28

Unifying Input and Output Smoothing in Neural Machine Transl...

引用

28th International Conference on Computational Linguistics, COLING 2020

作者： Gao, Yingbo Liao, Baohao Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)9781952148279

Soft contextualized data augmentation is a recent method that replaces one-hot representation of words with soft posterior distributions of an external language model, smoothing the input of neural machine translation systems. Label smoothing is another effective method that penalizes over-confident model outputs by discounting some probability mass from the true target word, smoothing the output of neural machine translation systems. Having the benefit of updating all word vectors in each optimization step and better regularizing the models, the two smoothing methods are shown to bring significant improvements in translation performance. In this work, we study how to best combine the methods and stack the improvements. Specifically, we vary the prior distributions to smooth with, the hyperparameters that control the smoothing strength, and the token selection procedures. We conduct extensive experiments on small datasets, evaluate the recipes on larger datasets, and examine the implications when back-translation is further used. Our results confirm cumulative improvements when input and output smoothing are used in combination, giving up to +1.9 BLEU scores on standard machine translation tasks and reveal reasons why these smoothing methods should be preferred. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Neural machine translation

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Biasing attention-based recurrent neural networks using external alignment information 2

Biasing attention-based recurrent neural networks using exte...

引用

2nd Conference on Machine Translation, WMT 2017

作者： Alkhouli, Tamer Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)9781945626968

This work explores extending attention-based neural models to include alignment information as input. We modify the attention component to have dependence on the current source position. The attention model is then used as a lexical model together with an additional alignment model to generate translation. The attention model is trained using external alignment information, and it is applied in decoding by performing beam search over the lexical and alignment hypotheses. The alignment model is used to score these alignment candidates. We demonstrate that the attention layer is capable of using the alignment information to improve over the baseline attention model that uses no such alignments. Our experiments are performed on two tasks: WMT 2016 English?Romanian and WMT 2017 German?English. © 2017 Association for Computational Linguistics

关键词： Recurrent neural networks

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Neural language Modeling for Named Entity recognition 28

Neural Language Modeling for Named Entity Recognition

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28th International Conference on Computational Linguistics, COLING 2020

作者： Lei, Zhihong Wang, Weiyue Dugast, Christian Ney, Hermann Apple Inc. Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Germany

ISBN: (纸本)9781952148279

Regardless of different word embedding and hidden layer structures of the neural architectures that are used in named entity recognition, a conditional random field layer is commonly used for the output. This work proposes to use a neural language model as an alternative to the conditional random field layer, which is more flexible for the size of the corpus. Experimental results show that the proposed system has a significant advantage in terms of training speed. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Modeling languages

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Improving Long Context Document-Level Machine Translation 4

Improving Long Context Document-Level Machine Translation

引用

4th Workshop on Computational Approaches to Discourse, CODI 2023

作者： Herold, Christian Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)9781959429890

Document-level context for neural machine translation (NMT) is crucial to improve the translation consistency and cohesion, the translation of ambiguous inputs, as well as several other linguistic phenomena. Many works have been published on the topic of document-level NMT, but most restrict the system to only local context, typically including just the one or two preceding sentences as additional information. This might be enough to resolve some ambiguous inputs, but it is probably not sufficient to capture some document-level information like the topic or style of a conversation. When increasing the context size beyond just the local context, there are two challenges: (i) the memory usage increases exponentially (ii) the translation performance starts to degrade. We argue that the widely-used attention mechanism is responsible for both issues. Therefore, we propose a constrained attention variant that focuses the attention on the most relevant parts of the sequence, while simultaneously reducing the memory consumption. For evaluation, we utilize targeted test sets in combination with novel evaluation techniques to analyze the translations in regards to specific discourse-related phenomena. We find that our approach is a good compromise between sentence-level NMT vs attending to the full context, especially in low resource scenarios. © 2023 Association for Computational Linguistics.

关键词： Neural machine translation

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Pivot lightly-supervised training for statistical machine translation 10

Pivot lightly-supervised training for statistical machine tr...

引用

10th Conference of the Association for Machine Translation in the Americas, AMTA 2012

作者： Huck, Matthias Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

In this paper, we investigate large-scale lightly-supervised training with a pivot language: We augment a baseline statistical machine translation (SMT) system that has been trained on human-generated parallel training corpora with large amounts of additional unsupervised parallel data;but instead of creating this synthetic data from monolingual source language data with the baseline system itself, or from target language data with a reverse system, we employ a parallel corpus of target language data and data in a pivot language. The pivot language data is automatically translated into the source language, resulting in a trilingual corpus with unsupervised source language side. We augment our baseline system with the unsupervised sourcetarget parallel data. Experiments are conducted for the German- French language pair using the standard WMT newstest sets for development and testing. We obtain the unsupervised data by translating the English side of the English-French 109 corpus to German. With careful system design, we are able to achieve improvements of up to +0.4 points BLEU / -0.7 points TER over the baseline.

关键词： computer aided language translation

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The rwth aachen german-english machine translation system for wmt 2015 10

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

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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： Peter, Jan-Thorsten Toutounchi, Farzad Wuebker, Joern Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department Rwth Aachen University AachenD-52056 Germany

ISBN: (纸本)9781941643327

This paper describes the statistical machine translation system developed at RWTH Aachen University for the German!English translation task of the EMNLP 2015 Tenth Workshop on Statistical Machine Translation (WMT 2015). A phrase-based machine translation system was applied and augmented with hierarchical phrase reordering and word class language models. Further, we ran discriminative maximum expected BLEU training for our system. In addition, we utilized multiple feed-forward neural network language and translation models and a recurrent neural network language model for reranking. © EMNLP 2015. All rights reserved.

关键词： Feedforward neural networks

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