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检索条件"机构=Human Language Technology And Pattern Recognition Group"

共 398 条记录，以下是11-20 订阅

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Phrase Model Training for Statistical Machine Translation with Word Lattices of Preprocessing Alternatives 7

Phrase Model Training for Statistical Machine Translation wi...

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 Ney, Hermann Human Language Technology And Pattern Recognition Group Rwth Aachen University Aachen Germany

ISBN: (纸本)9781937284206

In statistical machine translation, word lattices are used to represent the ambiguities in the preprocessing of the source sentence, such as word segmentation for Chinese or morphological analysis for German. Several approaches have been proposed to define the probability of different paths through the lattice with external tools like word segmenters, or by applying indicator features. We introduce a novel lattice design, which explicitly distinguishes between different preprocessing alternatives for the source sentence. It allows us to make use of specific features for each preprocessing type and to lexicalize the choice of lattice path directly in the phrase translation model. We argue that forced alignment training can be used to learn lattice path and phrase translation model simultaneously. On the newscommentary portion of the German!English WMT 2011 task we can show moderate improvements of up to 0.6% BLEU over a stateof- the-art baseline system. ©2012 Association for Computational Linguistics.

关键词： Computer aided language translation

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A Combination of Hierarchical Systems with Forced Alignments from Phrase-Based Systems 7

A Combination of Hierarchical Systems with Forced Alignments...

引用

7th International Workshop on Spoken language Translation, IWSLT 2010

作者： Heger, Carmen Wuebker, Joern Vilar, David Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

Currently most state-of-the-art statistical machine translation systems present a mismatch between training and generation conditions. Word alignments are computed using the well known IBM models for single-word based translation. After-wards phrases are extracted using extraction heuristics, unrelated to the stochastic models applied for finding the word alignment. In the last years, several research groups have tried to overcome this mismatch, but only with limited success. Recently, the technique of forced alignments has shown to improve translation quality for a phrase-based system, applying a more statistically sound approach to phrase extraction. In this work we investigate the first steps to combine forced alignment with a hierarchical model. Experimental results on IWSLT and WMT data show improvements in translation quality of up to 0.7% BLEU and 1.0% TER. © IWSLT 2010. All rights reserved.

关键词： Alignment

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Length-incremental Phrase Training for SMT 8

Length-incremental Phrase Training for SMT

引用

8th Workshop on Statistical Machine Translation, WMT 2013

作者： Wuebker, Joern Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (纸本)9781937284572

We present an iterative technique to generate phrase tables for SMT, which is based on force-aligning the training data with a modified translation decoder. Different from previous work, we completely avoid the use of a word alignment or phrase extraction heuristics, moving towards a more principled phrase generation and probability estimation. During training, we allow the decoder to generate new phrases on-the-fly and increment the maximum phrase length in each iteration. Experiments are carried out on the IWSLT 2011 Arabic-English task, where we are able to reach moderate improvements on a state-of-the-art baseline with our training method. The resulting phrase table shows only a small overlap with the heuristically extracted one, which demonstrates the restrictiveness of limiting phrase selection by a word alignment or heuristics. By interpolating the heuristic and the trained phrase table, we can improve over the baseline by 0.5% BLEU and 0.5% TER. © 2013 Association for Computational Linguistics

关键词： Decoding

来源：评论

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Extended translation models in phrase-based decoding 10

Extended translation models in phrase-based decoding

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： Guta, Andreas Wuebker, Joern Graça, Miguel Kim, Yunsu Ney, Hermann Human Language Technology and Pattern Recognition Group Rwth Aachen University Aachen Germany

ISBN: (纸本)9781941643327

We propose a novel extended translation model (ETM) to counteract some problems in phrase-based translation: The lack of translation context when using singleword phrases and uncaptured dependencies beyond phrase boundaries. The ETM operates on word-level and augments the IBM models by an additional bilingual word pair and a reordering operation. Its implementation in a phrase-based decoder introduces translation and reordering dependencies for single-word phrases and dependencies across phrase boundaries. More, the model incorporates an explicit treatment of multiple and empty alignments. Its integration outperforms competitive systems that include lexical and phrase translation models as well as hierarchical reordering models on 4 language pairs significantly by +0.7% BLEU on average. Although simpler and using fewer dependencies, the ETM proves to be on par with 7-gram operation sequence models (Durrani et al., 2013b). © EMNLP 2015. All rights reserved.

关键词： Decoding

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When and why is document-level context useful in neural machine translation? 4

When and why is document-level context useful in neural mach...

引用

4th Workshop on Discourse in Machine Translation, DiscoMT@EMNLP 2019

作者： Kim, Yunsu Tran, Duc Thanh Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (纸本)9781950737741

Document-level context has received lots of attention for compensating neural machine translation (NMT) of isolated sentences. However, recent advances in document-level NMT focus on sophisticated integration of the context, explaining its improvement with only a few selected examples or targeted test sets. We extensively quantify the causes of improvements by a document-level model in general test sets, clarifying the limit of the usefulness of document-level context in NMT. We show that most of the improvements are not interpretable as utilizing the context. We also show that a minimal encoding is sufficient for the context modeling and very long context is not helpful for NMT. © 2019 Association for Computational Linguistics.

关键词： Neural machine translation

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FIRE in ImageCLEF 2007

FIRE in ImageCLEF 2007

引用

2007 Cross language Evaluation Forum Workshop, CLEF 2007, co-located with the 11th European Conference on Digital Libraries, ECDL 2007

作者： Deselaers, Thomas Gass, Tobias Weyand, Tobias Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

We present the methods we applied in the four different tasks of the ImageCLEF 2007 content-based image retrieval evaluation. We participated in all four tasks using a variety of methods. Global and local image descriptors are applied using nearest neighbour search for the medical and photo retrieval tasks and discriminative models for the object retrieval and the medical automatic annotation task. For the photo and medical retrieval task, we apply a maximum entropy training method to learn an optimal feature weighting from the queries and qrels from last year. This method works particularly well if the queries are very similar as they were in the medical retrieval task.

关键词： Nearest neighbor search

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Alignment-Based Neural Machine Translation 1

Alignment-Based Neural Machine Translation

引用

1st Conference on Machine Translation, WMT 2016, held at the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016

作者： Alkhouli, Tamer Bretschner, Gabriel Peter, Jan-Thorsten Hethnawi, Mohammed Guta, Andreas Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (纸本)9781945626104

Neural machine translation (NMT) has emerged recently as a promising statistical machine translation approach. In NMT, neural networks (NN) are directly used to produce translations, without relying on a pre-existing translation framework. In this work, we take a step towards bridging the gap between conventional word alignment models and NMT. We follow the hidden Markov model (HMM) approach that separates the alignment and lexical models. We propose a neural alignment model and combine it with a lexical neural model in a log-linear framework. The models are used in a standalone word-based decoder that explicitly hypothesizes alignments during search. We demonstrate that our system outperforms attention-based NMT on two tasks: IWSLT 2013 German→English and BOLT Chinese→English. We also show promising results for re-aligning the training data using neural models. © 2016 Association for Computational Linguistics

关键词： Neural machine translation

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A comparison between count and neural network models based on joint translation and reordering sequences

A comparison between count and neural network models based o...

引用

Conference on Empirical Methods in Natural language Processing, EMNLP 2015

作者： Guta, Andreas Alkhouli, Tamer Peter, Jan-Thorsten Wuebker, Joern Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (纸本)9781941643327

We propose a conversion of bilingual sentence pairs and the corresponding word alignments into novel linear sequences. These are joint translation and reordering (JTR) uniquely defined sequences, combining interdepending lexical and alignment dependencies on the word level into a single framework. They are constructed in a simple manner while capturing multiple alignments and empty words. JTR sequences can be used to train a variety of models. We investigate the performances of ngram models with modified Kneser-Ney smoothing, feed-forward and recurrent neural network architectures when estimated on JTR sequences, and compare them to the operation sequence model (Durrani et al., 2013b). Evaluations on the IWSLT German→English, WMT German→English and BOLT Chinese→English tasks show that JTR models improve state-of-the-art phrasebased systems by up to 2.2 BLEU. © 2015 Association for Computational Linguistics.

关键词： Alignment

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Improving statistical machine translation with word class models

Improving statistical machine translation with word class mo...

引用

2013 Conference on Empirical Methods in Natural language Processing, EMNLP 2013

作者： Wuebker, Joern Peitz, Stephan Rietig, Felix Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (纸本)9781937284978

Automatically clustering words from a monolingual or bilingual training corpus into classes is a widely used technique in statistical natural language processing. We present a very simple and easy to implement method for using these word classes to improve translation quality. It can be applied across different machine translation paradigms and with arbitrary types of models. We show its efficacy on a small German → English and a larger French → German translation task with both standard phrase-based and hierarchical phrase-based translation systems for a common set of models. Our results show that with word class models, the baseline can be improved by up to 1.4% BLEU and 1.0% TER on the French → German task and 0.3% BLEU and 1.1% TER on the German → English task. © 2013 Association for Computational Linguistics.

关键词： Machine translation

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Investigations on phrase-based decoding with recurrent neural network language and translation models 10

Investigations on phrase-based decoding with recurrent neura...

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： Alkhouli, Tamer Rietig, Felix Ney, Hermann Human Language Technology and Pattern Recognition Group Rwth Aachen University Aachen Germany

ISBN: (纸本)9781941643327

This work explores the application of recurrent neural network (RNN) language and translation models during phrasebased decoding. Due to their use of unbounded context, the decoder integration of RNNs is more challenging compared to the integration of feedforward neural models. In this paper, we apply approximations and use caching to enable RNN decoder integration, while requiring reasonable memory and time resources. We analyze the effect of caching on translation quality and speed, and use it to integrate RNN language and translation models into a phrase-based decoder. To the best of our knowledge, no previous work has discussed the integration of RNN translation models into phrase-based decoding. We also show that a special RNN can be integrated efficiently without the need for approximations. We compare decoding using RNNs to rescoring n-best lists on two tasks: IWSLT 2013 German→English, and BOLT Arabic→English. We demonstrate that the performance of decoding with RNNs is at least as good as using them in rescoring. © EMNLP 2015. All rights reserved.

关键词： Recurrent neural networks

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