Matthias Huck

2024

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Prompting Large Language Models with Human Error Markings for Self-Correcting Machine Translation
Nathaniel Berger | Stefan Riezler | Miriam Exel | Matthias Huck
Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1)

While large language models (LLMs) pre-trained on massive amounts of unpaired language data have reached the state-of-the-art in machine translation (MT) of general domain texts, post-editing (PE) is still required to correct errors and to enhance term translation quality in specialized domains. In this paper we present a pilot study of enhancing translation memories (TM) produced by PE (source segments, machine translations, and reference translations, henceforth called PE-TM) for the needs of correct and consistent term translation in technical domains. We investigate a light-weight two-step scenario where at inference time, a human translator marks errors in the first translation step, and in a second step a few similar examples are extracted from the PE-TM to prompt an LLM. Our experiment shows that the additional effort of augmenting translations with human error markings guides the LLM to focus on a correction of the marked errors, yielding consistent improvements over automatic PE (APE) and MT from scratch.

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Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing
Sai Koneru | Miriam Exel | Matthias Huck | Jan Niehues
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Large language models (LLMs) have demonstrated considerable success in various natural language processing tasks, but open-source LLMs have yet to attain state-of-the-art performance in Neural Machine Translation (NMT). Nevertheless, their significant performance in tasks demanding a broad understanding and contextual processing shows their potential for translation. To exploit these abilities, we investigate using LLMs for MT and explore recent parameter-efficient fine-tuning techniques. Surprisingly, our initial experiments found that fine-tuning with Q-LoRA for translation purposes led to performance improvements in terms of BLEU but degradation in COMET compared to in-context learning. To overcome this, we propose an alternative approach: adapting LLMs as Automatic Post-Editors (APE) rather than direct translators. Building on the ability of the LLM to handle long sequences, we also propose extending our approach to document-level translation. We show that leveraging Low-Rank-Adapter fine-tuning for APE can yield significant improvements across both sentence and document-level metrics while generalizing to out-of-domain data. Most notably, we achieve a state-of-the-art accuracy rate of 88.7% on the ContraPro test set, which assesses the model’s ability to resolve pronoun ambiguities when translating from English to German. Lastly, during manual post-editing for document-level translation, the source sentences are iteratively annotated, which can be used to refine further translations in the document. Here, we demonstrate that leveraging human corrections can significantly reduce the number of edits required for subsequent translations.

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Post-edits Are Preferences Too
Nathaniel Berger | Stefan Riezler | Miriam Exel | Matthias Huck
Proceedings of the Ninth Conference on Machine Translation

Preference Optimization (PO) techniques are currently one of the state of the art techniques for fine-tuning large language models (LLMs) on pairwise preference feedback from human annotators. However, in machine translation, this sort of feedback can be difficult to solicit. Additionally, Kreuzer et al. (2018) have shown that, for machine translation, pairwise preferences are less reliable than other forms of human feedback, such as 5-point ratings.We examine post-edits to see if they can be a source of reliable human preferences by construction. In PO, a human annotator is shown sequences $s_1$ and $s_2$ and asked for a preference judgment, while for post-editing, editors create $s_1$ and know that it should be better than $s_2$. We attempt to use these implicit preferences for PO and show that it helps the model move towards post-edit like hypotheses and away from machine translation-like hypotheses. Furthermore, we show that best results are obtained by pre-training the model with supervised fine-tuning (SFT) on post-edits in order to promote post-edit like hypotheses to the top output ranks.

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Plug, Play, and Fuse: Zero-Shot Joint Decoding via Word-Level Re-ranking across Diverse Vocabularies
Sai Koneru | Matthias Huck | Miriam Exel | Jan Niehues
Proceedings of the Ninth Conference on Machine Translation

Recent advancements in NLP have resulted in models with specialized strengths, such as processing multimodal inputs or excelling in specific domains. However, real-world tasks, like multimodal translation, often require a combination of these strengths, such as handling both translation and image processing. While individual translation and vision models are powerful, they typically lack the ability to perform both tasks in a single system. Combining these models poses challenges, particularly due to differences in their vocabularies, which limit the effectiveness of traditional ensemble methods to post-generation techniques like N-best list re-ranking. In this work, we propose a novel zero-shot ensembling strategy that allows for the integration of different models during the decoding phase without the need for additional training. Our approach re-ranks beams during decoding by combining scores at the word level, using heuristics to predict when a word is completed. We demonstrate the effectiveness of this method in machine translation scenarios, showing that it enables the generation of translations that are both speech- and image-aware while also improving overall translation quality.

2023

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Analyzing Challenges in Neural Machine Translation for Software Localization
Sai Koneru | Matthias Huck | Miriam Exel | Jan Niehues
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Advancements in Neural Machine Translation (NMT) greatly benefit the software localization industry by decreasing the post-editing time of human annotators. Although the volume of the software being localized is growing significantly, techniques for improving NMT for user interface (UI) texts are lacking. These UI texts have different properties than other collections of texts, presenting unique challenges for NMT. For example, they are often very short, causing them to be ambiguous and needing additional context (button, title text, a table item, etc.) for disambiguation. However, no such UI data sets are readily available with contextual information for NMT models to exploit. This work aims to provide a first step in improving UI translations and highlight its challenges. To achieve this, we provide a novel multilingual UI corpus collection (∼ 1.3M for English ↔ German) with a targeted test set and analyze the limitations of state-of-the-art methods on this challenging task. Specifically, we present a targeted test set for disambiguation from English to German to evaluate reliably and emphasize UI translation challenges. Furthermore, we evaluate several state-of-the-art NMT techniques from domain adaptation and document-level NMT on this challenging task. All the scripts to replicate the experiments and data sets are available here.ˆ,

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Enhancing Supervised Learning with Contrastive Markings in Neural Machine Translation Training
Nathaniel Berger | Miriam Exel | Matthias Huck | Stefan Riezler
Proceedings of the 24th Annual Conference of the European Association for Machine Translation

Supervised learning in Neural Machine Translation (NMT) standardly follows a teacher forcing paradigm where the conditioning context in the model’s prediction is constituted by reference tokens, instead of its own previous predictions. In order to alleviate this lack of exploration in the space of translations, we present a simple extension of standard maximum likelihood estimation by a contrastive marking objective. The additional training signals are extracted automatically from reference translations by comparing the system hypothesis against the reference, and used for up/down-weighting correct/incorrect tokens. The proposed new training procedure requires one additional translation pass over the training set, and does not alter the standard inference setup. We show that training with contrastive markings yields improvements on top of supervised learning, and is especially useful when learning from postedits where contrastive markings indicate human error corrections to the original hypotheses.

2022

Translation of structured content is an important application of machine translation, but the scarcity of evaluation data sets, especially for Asian languages, limits progress. In this paper we present a novel multilingual multiway evaluation data set for the translation of structured documents of the Asian languages Japanese, Korean and Chinese. We describe the data set, its creation process and important characteristics, followed by establishing and evaluating baselines using the direct translation as well as detag-project approaches. Our data set is well suited for multilingual evaluation, and it contains richer annotation tag sets than existing data sets. Our results show that massively multilingual translation models like M2M-100 and mBART-50 perform surprisingly well despite not being explicitly trained to handle structured content. The data set described in this paper and used in our experiments is released publicly.

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“Hi, how can I help you?” Improving Machine Translation of Conversational Content in a Business Context
Bianka Buschbeck | Jennifer Mell | Miriam Exel | Matthias Huck
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

This paper addresses the automatic translation of conversational content in a business context, for example support chat dialogues. While such use cases share characteristics with other informal machine translation scenarios, translation requirements with respect to technical and business-related expressions are high. To succeed in such scenarios, we experimented with curating dedicated training and test data, injecting noise to improve robustness, and applying sentence weighting schemes to carefully manage the influence of the different corpora. We show that our approach improves the performance of our models on conversational content for all 18 investigated language pairs while preserving translation quality on other domains - an indispensable requirement to integrate these developments into our MT engines at SAP.

2021

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A Comparison of Sentence-Weighting Techniques for NMT
Simon Rieß | Matthias Huck | Alex Fraser
Proceedings of Machine Translation Summit XVIII: Research Track

Sentence weighting is a simple and powerful domain adaptation technique. We carry out domain classification for computing sentence weights with 1) language model cross entropy difference 2) a convolutional neural network 3) a Recursive Neural Tensor Network. We compare these approaches with regard to domain classification accuracy and and study the posterior probability distributions. Then we carry out NMT experiments in the scenario where we have no in-domain parallel corpora and and only very limited in-domain monolingual corpora. Here and we use the domain classifier to reweight the sentences of our out-of-domain training corpus. This leads to improvements of up to 2.1 BLEU for German to English translation.

This paper presents the results of the newstranslation task, the multilingual low-resourcetranslation for Indo-European languages, thetriangular translation task, and the automaticpost-editing task organised as part of the Con-ference on Machine Translation (WMT) 2021.In the news task, participants were asked tobuild machine translation systems for any of10 language pairs, to be evaluated on test setsconsisting mainly of news stories. The taskwas also opened up to additional test suites toprobe specific aspects of translation.

2020

This paper presents the results of the news translation task and the similar language translation task, both organised alongside the Conference on Machine Translation (WMT) 2020. In the news task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting mainly of news stories. The task was also opened up to additional test suites to probe specific aspects of translation. In the similar language translation task, participants built machine translation systems for translating between closely related pairs of languages.

2019

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Better OOV Translation with Bilingual Terminology Mining
Matthias Huck | Viktor Hangya | Alexander Fraser
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Unseen words, also called out-of-vocabulary words (OOVs), are difficult for machine translation. In neural machine translation, byte-pair encoding can be used to represent OOVs, but they are still often incorrectly translated. We improve the translation of OOVs in NMT using easy-to-obtain monolingual data. We look for OOVs in the text to be translated and translate them using simple-to-construct bilingual word embeddings (BWEs). In our MT experiments we take the 5-best candidates, which is motivated by intrinsic mining experiments. Using all five of the proposed target language words as queries we mine target-language sentences. We then back-translate, forcing the back-translation of each of the five proposed target-language OOV-translation-candidates to be the original source-language OOV. We show that by using this synthetic data to fine-tune our system the translation of OOVs can be dramatically improved. In our experiments we use a system trained on Europarl and mine sentences containing medical terms from monolingual data.

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Cross-lingual Annotation Projection Is Effective for Neural Part-of-Speech Tagging
Matthias Huck | Diana Dutka | Alexander Fraser
Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects

We tackle the important task of part-of-speech tagging using a neural model in the zero-resource scenario, where we have no access to gold-standard POS training data. We compare this scenario with the low-resource scenario, where we have access to a small amount of gold-standard POS training data. Our experiments focus on Ukrainian as a representative of under-resourced languages. Russian is highly related to Ukrainian, so we exploit gold-standard Russian POS tags. We consider four techniques to perform Ukrainian POS tagging: zero-shot tagging and cross-lingual annotation projection (for the zero-resource scenario), and compare these with self-training and multilingual learning (for the low-resource scenario). We find that cross-lingual annotation projection works particularly well in the zero-resource scenario.

This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019. Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation.

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The LMU Munich Unsupervised Machine Translation System for WMT19
Dario Stojanovski | Viktor Hangya | Matthias Huck | Alexander Fraser
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

We describe LMU Munich’s machine translation system for German→Czech translation which was used to participate in the WMT19 shared task on unsupervised news translation. We train our model using monolingual data only from both languages. The final model is an unsupervised neural model using established techniques for unsupervised translation such as denoising autoencoding and online back-translation. We bootstrap the model with masked language model pretraining and enhance it with back-translations from an unsupervised phrase-based system which is itself bootstrapped using unsupervised bilingual word embeddings.

2018

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Neural Morphological Tagging of Lemma Sequences for Machine Translation
Costanza Conforti | Matthias Huck | Alexander Fraser
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2018. Participants were asked to build machine translation systems for any of 7 language pairs in both directions, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. This year, we also opened up the task to additional test sets to probe specific aspects of translation.

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The LMU Munich Unsupervised Machine Translation Systems
Dario Stojanovski | Viktor Hangya | Matthias Huck | Alexander Fraser
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

We describe LMU Munich’s unsupervised machine translation systems for English↔German translation. These systems were used to participate in the WMT18 news translation shared task and more specifically, for the unsupervised learning sub-track. The systems are trained on English and German monolingual data only and exploit and combine previously proposed techniques such as using word-by-word translated data based on bilingual word embeddings, denoising and on-the-fly backtranslation.

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LMU Munich’s Neural Machine Translation Systems at WMT 2018
Matthias Huck | Dario Stojanovski | Viktor Hangya | Alexander Fraser
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

We present the LMU Munich machine translation systems for the English–German language pair. We have built neural machine translation systems for both translation directions (English→German and German→English) and for two different domains (the biomedical domain and the news domain). The systems were used for our participation in the WMT18 biomedical translation task and in the shared task on machine translation of news. The main focus of our recent system development efforts has been on achieving improvements in the biomedical domain over last year’s strong biomedical translation engine for English→German (Huck et al., 2017a). Considerable progress has been made in the latter task, which we report on in this paper.

2017

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Producing Unseen Morphological Variants in Statistical Machine Translation
Matthias Huck | Aleš Tamchyna | Ondřej Bojar | Alexander Fraser
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

Translating into morphologically rich languages is difficult. Although the coverage of lemmas may be reasonable, many morphological variants cannot be learned from the training data. We present a statistical translation system that is able to produce these inflected word forms. Different from most previous work, we do not separate morphological prediction from lexical choice into two consecutive steps. Our approach is novel in that it is integrated in decoding and takes advantage of context information from both the source language and the target language sides.

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Target-side Word Segmentation Strategies for Neural Machine Translation
Matthias Huck | Simon Riess | Alexander Fraser
Proceedings of the Second Conference on Machine Translation

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LMU Munich’s Neural Machine Translation Systems for News Articles and Health Information Texts
Matthias Huck | Fabienne Braune | Alexander Fraser
Proceedings of the Second Conference on Machine Translation

2016

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The Edinburgh/LMU Hierarchical Machine Translation System for WMT 2016
Matthias Huck | Alexander Fraser | Barry Haddow
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

The present work is an overview of the TraMOOC (Translation for Massive Open Online Courses) research and innovation project, a machine translation approach for online educational content. More specifically, videolectures, assignments, and MOOC forum text is automatically translated from English into eleven European and BRIC languages. Unlike previous approaches to machine translation, the output quality in TraMOOC relies on a multimodal evaluation schema that involves crowdsourcing, error type markup, an error taxonomy for translation model comparison, and implicit evaluation via text mining, i.e. entity recognition and its performance comparison between the source and the translated text, and sentiment analysis on the students’ forum posts. Finally, the evaluation output will result in more and better quality in-domain parallel data that will be fed back to the translation engine for higher quality output. The translation service will be incorporated into the Iversity MOOC platform and into the VideoLectures.net digital library portal.

2015

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Mixed domain vs. multi-domain statistical machine translation
Matthias Huck | Alexandra Birch | Barry Haddow
Proceedings of Machine Translation Summit XV: Papers

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The Edinburgh/JHU Phrase-based Machine Translation Systems for WMT 2015
Barry Haddow | Matthias Huck | Alexandra Birch | Nikolay Bogoychev | Philipp Koehn
Proceedings of the Tenth Workshop on Statistical Machine Translation

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Edinburgh’s Syntax-Based Systems at WMT 2015
Philip Williams | Rico Sennrich | Maria Nadejde | Matthias Huck | Philipp Koehn
Proceedings of the Tenth Workshop on Statistical Machine Translation

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The Edinburgh machine translation systems for IWSLT 2015
Matthias Huck | Alexandra Birch
Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign

2014

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Edinburgh SLT and MT system description for the IWSLT 2014 evaluation
Alexandra Birch | Matthias Huck | Nadir Durrani | Nikolay Bogoychev | Philipp Koehn
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the University of Edinburgh’s spoken language translation (SLT) and machine translation (MT) systems for the IWSLT 2014 evaluation campaign. In the SLT track, we participated in the German↔English and English→French tasks. In the MT track, we participated in the German↔English, English→French, Arabic↔English, Farsi→English, Hebrew→English, Spanish↔English, and Portuguese-Brazil↔English tasks. For our SLT submissions, we experimented with comparing operation sequence models with bilingual neural network language models. For our MT submissions, we explored using unsupervised transliteration for languages which have a different script than English, in particular for Arabic, Farsi, and Hebrew. We also investigated syntax-based translation and system combination.

EU-BRIDGE is a European research project which is aimed at developing innovative speech translation technology. One of the collaborative efforts within EU-BRIDGE is to produce joint submissions of up to four different partners to the evaluation campaign at the 2014 International Workshop on Spoken Language Translation (IWSLT). We submitted combined translations to the German→English spoken language translation (SLT) track as well as to the German→English, English→German and English→French machine translation (MT) tracks. In this paper, we present the techniques which were applied by the different individual translation systems of RWTH Aachen University, the University of Edinburgh, Karlsruhe Institute of Technology, and Fondazione Bruno Kessler. We then show the combination approach developed at RWTH Aachen University which combined the individual systems. The consensus translations yield empirical gains of up to 2.3 points in BLEU and 1.2 points in TER compared to the best individual system.

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Augmenting String-to-Tree and Tree-to-String Translation with Non-Syntactic Phrases
Matthias Huck | Hieu Hoang | Philipp Koehn
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Preference Grammars and Soft Syntactic Constraints for GHKM Syntax-based Statistical Machine Translation
Matthias Huck | Hieu Hoang | Philipp Koehn
Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation

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Jane: Open Source Machine Translation System Combination
Markus Freitag | Matthias Huck | Hermann Ney
Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Statistical machine translation with the Moses toolkit
Hieu Hoang | Matthias Huck | Philipp Koehn
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: Tutorials

2013

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A Performance Study of Cube Pruning for Large-Scale Hierarchical Machine Translation
Matthias Huck | David Vilar | Markus Freitag | Hermann Ney
Proceedings of the Seventh Workshop on Syntax, Semantics and Structure in Statistical Translation

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A Phrase Orientation Model for Hierarchical Machine Translation
Matthias Huck | Joern Wuebker | Felix Rietig | Hermann Ney
Proceedings of the Eighth Workshop on Statistical Machine Translation

EU-BRIDGE1 is a European research project which is aimed at developing innovative speech translation technology. This paper describes one of the collaborative efforts within EUBRIDGE to further advance the state of the art in machine translation between two European language pairs, English→French and German→English. Four research institutions involved in the EU-BRIDGE project combined their individual machine translation systems and participated with a joint setup in the machine translation track of the evaluation campaign at the 2013 International Workshop on Spoken Language Translation (IWSLT). We present the methods and techniques to achieve high translation quality for text translation of talks which are applied at RWTH Aachen University, the University of Edinburgh, Karlsruhe Institute of Technology, and Fondazione Bruno Kessler. We then show how we have been able to considerably boost translation performance (as measured in terms of the metrics BLEU and TER) by means of system combination. The joint setups yield empirical gains of up to 1.4 points in BLEU and 2.8 points in TER on the IWSLT test sets compared to the best single systems.

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Reverse Word Order Model
Markus Freitag | Minwei Feng | Matthias Huck | Stephan Peitz | Hermann Ney
Proceedings of Machine Translation Summit XIV: Papers

2012

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Insertion and Deletion Models for Statistical Machine Translation
Matthias Huck | Hermann Ney
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

In this paper, the automatic speech recognition (ASR) and statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2012 are presented. We participated in the ASR (English), MT (English-French, Arabic-English, Chinese-English, German-English) and SLT (English-French) tracks. For the MT track both hierarchical and phrase-based SMT decoders are applied. A number of different techniques are evaluated in the MT and SLT tracks, including domain adaptation via data selection, translation model interpolation, phrase training for hierarchical and phrase-based systems, additional reordering model, word class language model, various Arabic and Chinese segmentation methods, postprocessing of speech recognition output with an SMT system, and system combination. By application of these methods we can show considerable improvements over the respective baseline systems.

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The RWTH Aachen Machine Translation System for WMT 2012
Matthias Huck | Stephan Peitz | Markus Freitag | Malte Nuhn | Hermann Ney
Proceedings of the Seventh Workshop on Statistical Machine Translation

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Pivot Lightly-Supervised Training for Statistical Machine Translation
Matthias Huck | Hermann Ney
Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers

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 source-target 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.

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Discriminative Reordering Extensions for Hierarchical Phrase-Based Machine Translation
Matthias Huck | Stephan Peitz | Markus Freitag | Hermann Ney
Proceedings of the 16th Annual Conference of the European Association for Machine Translation

2011

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Advancements in Arabic-to-English Hierarchical Machine Translation
Matthias Huck | David Vilar | Daniel Stein | Hermann Ney
Proceedings of the 15th Annual Conference of the European Association for Machine Translation

In this paper the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2011 is presented. We participated in the MT (English-French, Arabic-English, ChineseEnglish) and SLT (English-French) tracks. Both hierarchical and phrase-based SMT decoders are applied. A number of different techniques are evaluated, including domain adaptation via monolingual and bilingual data selection, phrase training, different lexical smoothing methods, additional reordering models for the hierarchical system, various Arabic and Chinese segmentation methods, punctuation prediction for speech recognition output, and system combination. By application of these methods we can show considerable improvements over the respective baseline systems.

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Lexicon models for hierarchical phrase-based machine translation
Matthias Huck | Saab Mansour | Simon Wiesler | Hermann Ney
Proceedings of the 8th International Workshop on Spoken Language Translation: Papers

In this paper, we investigate lexicon models for hierarchical phrase-based statistical machine translation. We study five types of lexicon models: a model which is extracted from word-aligned training data and—given the word alignment matrix—relies on pure relative frequencies [1]; the IBM model 1 lexicon [2]; a regularized version of IBM model 1; a triplet lexicon model variant [3]; and a discriminatively trained word lexicon model [4]. We explore sourceto-target models with phrase-level as well as sentence-level scoring and target-to-source models with scoring on phrase level only. For the first two types of lexicon models, we compare several scoring variants. All models are used during search, i.e. they are incorporated directly into the log-linear model combination of the decoder. Phrase table smoothing with triplet lexicon models and with discriminative word lexicons are novel contributions. We also propose a new regularization technique for IBM model 1 by means of the Kullback-Leibler divergence with the empirical unigram distribution as regularization term. Experiments are carried out on the large-scale NIST Chinese→English translation task and on the English→French and Arabic→English IWSLT TED tasks. For Chinese→English and English→French, we obtain the best results by using the discriminative word lexicon to smooth our phrase tables.

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Soft string-to-dependency hierarchical machine translation
Jan-Thorsten Peter | Matthias Huck | Hermann Ney | Daniel Stein
Proceedings of the 8th International Workshop on Spoken Language Translation: Papers

In this paper, we dissect the influence of several target-side dependency-based extensions to hierarchical machine translation, including a dependency language model (LM). We pursue a non-restrictive approach that does not prohibit the production of hypotheses with malformed dependency structures. Since many questions remained open from previous and related work, we offer in-depth analysis of the influence of the language model order, the impact of dependency-based restrictions on the search space, and the information to be gained from dependency tree building during decoding. The application of a non-restrictive approach together with an integrated dependency LM scoring is a novel contribution which yields significant improvements for two large-scale translation tasks for the language pairs Chinese–English and German–French.

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Lightly-Supervised Training for Hierarchical Phrase-Based Machine Translation
Matthias Huck | David Vilar | Daniel Stein | Hermann Ney
Proceedings of the First workshop on Unsupervised Learning in NLP

2010

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A Comparison of Various Types of Extended Lexicon Models for Statistical Machine Translation
Matthias Huck | Martin Ratajczak | Patrick Lehnen | Hermann Ney
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers

In this work we give a detailed comparison of the impact of the integration of discriminative and trigger-based lexicon models in state-of-the-art hierarchical and conventional phrase-based statistical machine translation systems. As both types of extended lexicon models can grow very large, we apply certain restrictions to discard some of the less useful information. We show how these restrictions facilitate the training of the extended lexicon models. We finally evaluate systems that incorporate both types of models with different restrictions on a large-scale translation task for the Arabic-English language pair. Our results suggest that extended lexicon models can be substantially reduced in size while still giving clear improvements in translation performance.