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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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Neural language Modeling for Named Entity recognition 28

Neural Language Modeling for Named Entity Recognition

引用

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

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

引用

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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The RWTH Aachen University Supervised Machine Translation Systems for WMT 2018 3

The RWTH Aachen University Supervised Machine Translation Sy...

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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： Schamper, Julian Rosendahl, Jan Bahar, Parnia Kim, Yunsu Nix, Arne 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 statistical machine translation systems developed at RWTH Aachen University for the German→English, English→Turkish and Chinese→English translation tasks of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018). We use ensembles of neural machine translation systems based on the Transformer architecture. Our main focus is on the German→English task where we scored first with respect to all automatic metrics provided by the organizers. We identify data selection, fine-tuning, batch size and model dimension as important hyperparameters. In total we improve by 6.8% BLEU over our last year’s submission and by 4.8% BLEU over the winning system of the 2017 German→English task. In English→Turkish task, we show 3.6% BLEU improvement over the last year’s winning system. We further report results on the Chinese→English task where we improve 2.2% BLEU on average over our baseline systems but stay behind the 2018 winning systems. ©2018 Association for Computational Linguistics

关键词： Neural machine translation

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Leave-One-Out Phrase Model Training for Large-Scale Deployment 7

Leave-One-Out Phrase Model Training for Large-Scale Deployme...

引用

7th Workshop on Statistical Machine Translation, WMT 2012, immediately following the Conference of the North-American Chapter of the Association for Computational Linguistics - human language Technologies, NAACL HLT 2012

作者： Wuebker, Joern Hwang, Mei-Yuh Quirk, Chris Human Language Technology And Pattern Recognition Group Rwth Aachen University Germany Microsoft Corporation RedmondWA United States

ISBN: (纸本)9781937284206

Training the phrase table by force-aligning (FA) the training data with the reference translation has been shown to improve the phrasal translation quality while significantly reducing the phrase table size on medium sized tasks. We apply this procedure to several large-scale tasks, with the primary goal of reducing model sizes without sacrificing translation quality. To deal with the noise in the automatically crawled parallel training data, we introduce on-demand word deletions, insertions, and backoffs to achieve over 99% successful alignment rate. We also add heuristics to avoid any increase in OOV rates. We are able to reduce already heavily pruned baseline phrase tables by more than 50% with little to no degradation in quality and occasionally slight improvement, without any increase in OOVs. We further introduce two global scaling factors for re-estimation of the phrase table via posterior phrase alignment probabilities and a modified absolute discounting method that can be applied to fractional counts. ©2012 Association for Computational Linguistics.

关键词： Computational linguistics

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A Phrase Orientation Model for Hierarchical Machine Translation 8

A Phrase Orientation Model for Hierarchical Machine Translat...

引用

8th Workshop on Statistical Machine Translation, WMT 2013

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

ISBN: (纸本)9781937284572

We introduce a lexicalized reordering model for hierarchical phrase-based machine translation. The model scores monotone, swap, and discontinuous phrase orientations in the manner of the one presented by Tillmann (2004). While this type of lexicalized reordering model is a valuable and widely-used component of standard phrase-based statistical machine translation systems (Koehn et al., 2007), it is however commonly not employed in hierarchical decoders. We describe how phrase orientation probabilities can be extracted from word-aligned training data for use with hierarchical phrase inventories, and show how orientations can be scored in hierarchical decoding. The model is empirically evaluated on the NIST Chinese→English translation task. We achieve a significant improvement of +1.2 %BLEU over a typical hierarchical baseline setup and an improvement of +0.7 %BLEU over a syntax-augmented hierarchical setup. On a French→German translation task, we obtain a gain of up to +0.4 %BLEU. © 2013 Association for Computational Linguistics

关键词： Machine translation

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Does Joint Training Really Help Cascaded Speech Translation?

Does Joint Training Really Help Cascaded Speech Translation?

引用

2022 Conference on Empirical Methods in Natural language Processing, EMNLP 2022

作者： Tran, Viet Anh Khoa Thulke, David Gao, Yingbo Herold, Christian Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

Currently, in speech translation, the straightforward approach - cascading a recognition system with a translation system - delivers state-of-the-art results. However, fundamental challenges such as error propagation from the automatic speech recognition system still remain. To mitigate these problems, recently, people turn their attention to direct data and propose various joint training methods. In this work, we seek to answer the question of whether joint training really helps cascaded speech translation. We review recent papers on the topic and also investigate a joint training criterion by marginalizing the transcription posterior probabilities. Our findings show that a strong cascaded baseline can diminish any improvements obtained using joint training, and we suggest alternatives to joint training. We hope this work can serve as a refresher of the current speech translation landscape, and motivate research in finding more efficient and creative ways to utilize the direct data for speech translation. © 2022 Association for Computational Linguistics.

关键词： Speech recognition

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Investigation on estimation of sentence probability by combining forward, backward and Bi-directional LSTM-RNNs 19

Investigation on estimation of sentence probability by combi...

引用

19th Annual Conference of the International Speech Communication, INTERSPEECH 2018

作者： Irie, Kazuki Lei, Zhihong Deng, Liuhui Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

A combination of forward and backward long short-term memory (LSTM) recurrent neural network (RNN) language models is a popular model combination approach to improve the estimation of the sequence probability in the second pass N-best list rescoring in automatic speech recognition (ASR). In this work, we further push such an idea by proposing a combination of three models: a forward LSTM language model, a backward LSTM language model and a bi-directional LSTM based gap completion model. We derive such a combination method from a forward backward decomposition of the sequence probability. We carry out experiments on the Switchboard speech recognition task. While we empirically find that such a combination gives slight improvements in perplexity over the combination of forward and backward models, we finally show that a combination of the same number of forward models gives the best perplexity and word error rate (WER) overall. © 2018 International Speech Communication Association. All rights reserved.

关键词： Long short-term memory

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Discriminative reordering extensions for hierarchical phrase-based machine translation 16

Discriminative reordering extensions for hierarchical phrase...

引用

16th Annual Conference of the European Association for Machine Translation, EAMT 2012

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

In this paper, we propose novel extensions of hierarchical phrase-based systems with a discriminative lexicalized reordering model. We compare different feature sets for the discriminative reordering model and investigate combinations with three types of non-lexicalized reordering rules which are added to the hierarchical grammar in order to allow for more reordering flexibility during decoding. All extensions are evaluated in standard hierarchical setups as well as in setups where the hierarchical recursion depth is restricted. We achieve improvements of up to +1.2 %BLEU on a large-scale Chinese→English translation task. © 2012 European Association for Machine Translation.

关键词： Hierarchical systems

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