Mauro Cettolo


2021

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Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference?
Luisa Bentivogli | Mauro Cettolo | Marco Gaido | Alina Karakanta | Alberto Martinelli | Matteo Negri | Marco Turchi
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Five years after the first published proofs of concept, direct approaches to speech translation (ST) are now competing with traditional cascade solutions. In light of this steady progress, can we claim that the performance gap between the two is closed? Starting from this question, we present a systematic comparison between state-of-the-art systems representative of the two paradigms. Focusing on three language directions (English-German/Italian/Spanish), we conduct automatic and manual evaluations, exploiting high-quality professional post-edits and annotations. Our multi-faceted analysis on one of the few publicly available ST benchmarks attests for the first time that: i) the gap between the two paradigms is now closed, and ii) the subtle differences observed in their behavior are not sufficient for humans neither to distinguish them nor to prefer one over the other.

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Beyond Voice Activity Detection: Hybrid Audio Segmentation for Direct Speech Translation
Marco Gaido | Matteo Negri | Mauro Cettolo | Marco Turchi
Proceedings of The Fourth International Conference on Natural Language and Speech Processing (ICNLSP 2021)

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CTC-based Compression for Direct Speech Translation
Marco Gaido | Mauro Cettolo | Matteo Negri | Marco Turchi
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Previous studies demonstrated that a dynamic phone-informed compression of the input audio is beneficial for speech translation (ST). However, they required a dedicated model for phone recognition and did not test this solution for direct ST, in which a single model translates the input audio into the target language without intermediate representations. In this work, we propose the first method able to perform a dynamic compression of the input in direct ST models. In particular, we exploit the Connectionist Temporal Classification (CTC) to compress the input sequence according to its phonetic characteristics. Our experiments demonstrate that our solution brings a 1.3-1.5 BLEU improvement over a strong baseline on two language pairs (English-Italian and English-German), contextually reducing the memory footprint by more than 10%.

2018

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A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation
Surafel Melaku Lakew | Mauro Cettolo | Marcello Federico
Proceedings of the 27th International Conference on Computational Linguistics

Recently, neural machine translation (NMT) has been extended to multilinguality, that is to handle more than one translation direction with a single system. Multilingual NMT showed competitive performance against pure bilingual systems. Notably, in low-resource settings, it proved to work effectively and efficiently, thanks to shared representation space that is forced across languages and induces a sort of transfer-learning. Furthermore, multilingual NMT enables so-called zero-shot inference across language pairs never seen at training time. Despite the increasing interest in this framework, an in-depth analysis of what a multilingual NMT model is capable of and what it is not is still missing. Motivated by this, our work (i) provides a quantitative and comparative analysis of the translations produced by bilingual, multilingual and zero-shot systems; (ii) investigates the translation quality of two of the currently dominant neural architectures in MT, which are the Recurrent and the Transformer ones; and (iii) quantitatively explores how the closeness between languages influences the zero-shot translation. Our analysis leverages multiple professional post-edits of automatic translations by several different systems and focuses both on automatic standard metrics (BLEU and TER) and on widely used error categories, which are lexical, morphology, and word order errors.

2017

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Findings of the 2017 DiscoMT Shared Task on Cross-lingual Pronoun Prediction
Sharid Loáiciga | Sara Stymne | Preslav Nakov | Christian Hardmeier | Jörg Tiedemann | Mauro Cettolo | Yannick Versley
Proceedings of the Third Workshop on Discourse in Machine Translation

We describe the design, the setup, and the evaluation results of the DiscoMT 2017 shared task on cross-lingual pronoun prediction. The task asked participants to predict a target-language pronoun given a source-language pronoun in the context of a sentence. We further provided a lemmatized target-language human-authored translation of the source sentence, and automatic word alignments between the source sentence words and the target-language lemmata. The aim of the task was to predict, for each target-language pronoun placeholder, the word that should replace it from a small, closed set of classes, using any type of information that can be extracted from the entire document. We offered four subtasks, each for a different language pair and translation direction: English-to-French, English-to-German, German-to-English, and Spanish-to-English. Five teams participated in the shared task, making submissions for all language pairs. The evaluation results show that most participating teams outperformed two strong n-gram-based language model-based baseline systems by a sizable margin.

2016

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WAGS: A Beautiful English-Italian Benchmark Supporting Word Alignment Evaluation on Rare Words
Luisa Bentivogli | Mauro Cettolo | M. Amin Farajian | Marcello Federico
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents WAGS (Word Alignment Gold Standard), a novel benchmark which allows extensive evaluation of WA tools on out-of-vocabulary (OOV) and rare words. WAGS is a subset of the Common Test section of the Europarl English-Italian parallel corpus, and is specifically tailored to OOV and rare words. WAGS is composed of 6,715 sentence pairs containing 11,958 occurrences of OOV and rare words up to frequency 15 in the Europarl Training set (5,080 English words and 6,878 Italian words), representing almost 3% of the whole text. Since WAGS is focused on OOV/rare words, manual alignments are provided for these words only, and not for the whole sentences. Two off-the-shelf word aligners have been evaluated on WAGS, and results have been compared to those obtained on an existing benchmark tailored to full text alignment. The results obtained confirm that WAGS is a valuable resource, which allows a statistically sound evaluation of WA systems’ performance on OOV and rare words, as well as extensive data analyses. WAGS is publicly released under a Creative Commons Attribution license.

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Neural versus Phrase-Based Machine Translation Quality: a Case Study
Luisa Bentivogli | Arianna Bisazza | Mauro Cettolo | Marcello Federico
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Findings of the 2016 WMT Shared Task on Cross-lingual Pronoun Prediction
Liane Guillou | Christian Hardmeier | Preslav Nakov | Sara Stymne | Jörg Tiedemann | Yannick Versley | Mauro Cettolo | Bonnie Webber | Andrei Popescu-Belis
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

2015

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The IWSLT 2015 Evaluation Campaign
Mauro Cettolo | Jan Niehues | Sebastian Stüker | Luisa Bentivogli | Roldano Cattoni | Marcello Federico
Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign

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Pronoun-Focused MT and Cross-Lingual Pronoun Prediction: Findings of the 2015 DiscoMT Shared Task on Pronoun Translation
Christian Hardmeier | Preslav Nakov | Sara Stymne | Jörg Tiedemann | Yannick Versley | Mauro Cettolo
Proceedings of the Second Workshop on Discourse in Machine Translation

2014

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Proceedings of the 17th Annual conference of the European Association for Machine Translation
Mauro Cettolo | Marcello Federico | Lucia Specia | Andy Way
Proceedings of the 17th Annual conference of the European Association for Machine Translation

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The MateCat Tool
Marcello Federico | Nicola Bertoldi | Mauro Cettolo | Matteo Negri | Marco Turchi | Marco Trombetti | Alessandro Cattelan | Antonio Farina | Domenico Lupinetti | Andrea Martines | Alberto Massidda | Holger Schwenk | Loïc Barrault | Frederic Blain | Philipp Koehn | Christian Buck | Ulrich Germann
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations

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Report on the 11th IWSLT evaluation campaign
Mauro Cettolo | Jan Niehues | Sebastian Stüker | Luisa Bentivogli | Marcello Federico
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

The paper overviews the 11th evaluation campaign organized by the IWSLT workshop. The 2014 evaluation offered multiple tracks on lecture transcription and translation based on the TED Talks corpus. In particular, this year IWSLT included three automatic speech recognition tracks, on English, German and Italian, five speech translation tracks, from English to French, English to German, German to English, English to Italian, and Italian to English, and five text translation track, also from English to French, English to German, German to English, English to Italian, and Italian to English. In addition to the official tracks, speech and text translation optional tracks were offered, globally involving 12 other languages: Arabic, Spanish, Portuguese (B), Hebrew, Chinese, Polish, Persian, Slovenian, Turkish, Dutch, Romanian, Russian. Overall, 21 teams participated in the evaluation, for a total of 76 primary runs submitted. Participants were also asked to submit runs on the 2013 test set (progress test set), in order to measure the progress of systems with respect to the previous year. All runs were evaluated with objective metrics, and submissions for two of the official text translation tracks were also evaluated with human post-editing.

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Combined spoken language translation
Markus Freitag | Joern Wuebker | Stephan Peitz | Hermann Ney | Matthias Huck | Alexandra Birch | Nadir Durrani | Philipp Koehn | Mohammed Mediani | Isabel Slawik | Jan Niehues | Eunach Cho | Alex Waibel | Nicola Bertoldi | Mauro Cettolo | Marcello Federico
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

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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Online multi-user adaptive statistical machine translation
Prashant Mathur | Mauro Cettolo | Marcello Federico | José G.C. de Souza
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track

In this paper we investigate the problem of adapting a machine translation system to the feedback provided by multiple post-editors. It is well know that translators might have very different post-editing styles and that this variability hinders the application of online learning methods, which indeed assume a homogeneous source of adaptation data. We hence propose multi-task learning to leverage bias information from each single post-editors in order to constrain the evolution of the SMT system. A new framework for significance testing with sentence level metrics is described which shows that Multi-Task learning approaches outperforms existing online learning approaches, with significant gains of 1.24 and 1.88 TER score over a strong online adaptive baseline, on a test set of post-edits produced by four translators texts and on a popular benchmark with multiple references, respectively.

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The repetition rate of text as a predictor of the effectiveness of machine translation adaptation
Mauro Cettolo | Nicola Bertoldi | Marcello Federico
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track

Since the effectiveness of MT adaptation relies on the text repetitiveness, the question on how to measure repetitions in a text naturally arises. This work deals with the issue of looking for and evaluating text features that might help the prediction of the impact of MT adaptation on translation quality. In particular, the repetition rate metric, we recently proposed, is compared to other features employed in very related NLP tasks. The comparison is carried out through a regression analysis between feature values and MT performance gains by dynamically adapted versus non-adapted MT engines, on five different translation tasks. The main outcome of experiments is that the repetition rate correlates better than any other considered feature with the MT gains yielded by the online adaptation, although using all features jointly results in better predictions than with any single feature.

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Optimized MT online learning in computer assisted translation
Prashant Mathur | Mauro Cettolo
Workshop on interactive and adaptive machine translation

In this paper we propose a cascading framework for optimizing online learning in machine translation for a computer assisted translation scenario. With the use of online learning, several hyperparameters associated with the learning algorithm are introduced. The number of iterations of online learning can affect the translation quality as well. We discuss these issues and propose a few approaches to optimize the hyperparameters and to find the number of iterations required for online learning. We experimentally show that optimizing hyperparameters and number of iterations in online learning yields consistent improvement against baseline results.

2013

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Online Learning Approaches in Computer Assisted Translation
Prashant Mathur | Mauro Cettolo | Marcello Federico
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Project Adaptation for MT-Enhanced Computer Assisted Translation
Mauro Cettolo | Nicola Bertoldi | Marcello Federico
Proceedings of Machine Translation Summit XIV: Papers

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Cache-based Online Adaptation for Machine Translation Enhanced Computer Assisted Translation
Nicola Bertoldi | Mauro Cettolo | Marcello Federico
Proceedings of Machine Translation Summit XIV: Papers

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Issues in incremental adaptation of statistical MT from human post-edits
Mauro Cettolo | Christophe Servan | Nicola Bertoldi | Marcello Federico | Loïc Barrault | Holger Schwenk
Proceedings of the 2nd Workshop on Post-editing Technology and Practice

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Report on the 10th IWSLT evaluation campaign
Mauro Cettolo | Jan Niehues | Sebastian Stüker | Luisa Bentivogli | Marcello Federico
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

The paper overviews the tenth evaluation campaign organized by the IWSLT workshop. The 2013 evaluation offered multiple tracks on lecture transcription and translation based on the TED Talks corpus. In particular, this year IWSLT included two automatic speech recognition tracks, on English and German, three speech translation tracks, from English to French, English to German, and German to English, and three text translation track, also from English to French, English to German, and German to English. In addition to the official tracks, speech and text translation optional tracks were offered involving 12 other languages: Arabic, Spanish, Portuguese (B), Italian, Chinese, Polish, Persian, Slovenian, Turkish, Dutch, Romanian, Russian. Overall, 18 teams participated in the evaluation for a total of 217 primary runs submitted. All runs were evaluated with objective metrics on a current test set and two progress test sets, in order to compare the progresses against systems of the previous years. In addition, submissions of one of the official machine translation tracks were also evaluated with human post-editing.

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EU-BRIDGE MT: text translation of talks in the EU-BRIDGE project
Markus Freitag | Stephan Peitz | Joern Wuebker | Hermann Ney | Nadir Durrani | Matthias Huck | Philipp Koehn | Thanh-Le Ha | Jan Niehues | Mohammed Mediani | Teresa Herrmann | Alex Waibel | Nicola Bertoldi | Mauro Cettolo | Marcello Federico
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

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.

2012

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Proceedings of the 16th Annual conference of the European Association for Machine Translation
Mauro Cettolo | Marcello Federico | Lucia Specia | Andy Way
Proceedings of the 16th Annual conference of the European Association for Machine Translation

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WIT3: Web Inventory of Transcribed and Translated Talks
Mauro Cettolo | Christian Girardi | Marcello Federico
Proceedings of the 16th Annual conference of the European Association for Machine Translation

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The IWSLT 2011 Evaluation Campaign on Automatic Talk Translation
Marcello Federico | Sebastian Stüker | Luisa Bentivogli | Michael Paul | Mauro Cettolo | Teresa Herrmann | Jan Niehues | Giovanni Moretti
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We report here on the eighth evaluation campaign organized in 2011 by the IWSLT workshop series. That IWSLT 2011 evaluation focused on the automatic translation of public talks and included tracks for speech recognition, speech translation, text translation, and system combination. Unlike in previous years, all data supplied for the evaluation has been publicly released on the workshop website, and is at the disposal of researchers interested in working on our benchmarks and in comparing their results with those published at the workshop. This paper provides an overview of the IWSLT 2011 evaluation campaign, and describes the data supplied, the evaluation infrastructure made available to participants, and the subjective evaluation carried out.

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Evaluating the Learning Curve of Domain Adaptive Statistical Machine Translation Systems
Nicola Bertoldi | Mauro Cettolo | Marcello Federico | Christian Buck
Proceedings of the Seventh Workshop on Statistical Machine Translation

2011

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Methods for Smoothing the Optimizer Instability in SMT
Mauro Cettolo | Nicola Bertoldi | Marcello Federico
Proceedings of Machine Translation Summit XIII: Papers

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Bootstrapping Arabic-Italian SMT through Comparable Texts and Pivot Translation
Mauro Cettolo | Nicola Bertoldi | Marcello Federico
Proceedings of the 15th Annual conference of the European Association for Machine Translation

2010

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Online Language Model adaptation via N-gram Mixtures for Statistical Machine Translation
Germán Sanchis-Trilles | Mauro Cettolo
Proceedings of the 14th Annual conference of the European Association for Machine Translation

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FBK @ IWSLT 2010
Arianna Bisazza | Ioannis Klasinas | Mauro Cettolo | Marcello Federico
Proceedings of the 7th International Workshop on Spoken Language Translation: Evaluation Campaign

This year FBK took part in the BTEC translation task, with source languages Arabic and Turkish and target language English, and in the new TALK task, source English and target French. We worked in the framework of phrase-based statistical machine translation aiming to improve coverage of models in presence of rich morphology, on one side, and to make better use of available resources through data selection techniques. New morphological segmentation rules were developed for Turkish-English. The combination of several Turkish segmentation schemes into a lattice input led to an improvement wrt to last year. The use of additional training data was explored for Arabic-English, while on the English to French task improvement was achieved over a strong baseline by automatically selecting relevant and high quality data from the available training corpora.

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Mining parallel fragments from comparable texts
Mauro Cettolo | Marcello Federico | Nicola Bertoldi
Proceedings of the 7th International Workshop on Spoken Language Translation: Papers

This paper proposes a novel method for exploiting comparable documents to generate parallel data for machine translation. First, each source document is paired to each sentence of the corresponding target document; second, partial phrase alignments are computed within the paired texts; finally, fragment pairs across linked phrase-pairs are extracted. The algorithm has been tested on two recent challenging news translation tasks. Results show that mining for parallel fragments is more effective than mining for parallel sentences, and that comparable in-domain texts can be more valuable than parallel out-of-domain texts.

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Statistical Machine Translation of Texts with Misspelled Words
Nicola Bertoldi | Mauro Cettolo | Marcello Federico
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

2009

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FBK at IWSLT 2009
Nicola Bertoldi | Arianna Bisazza | Mauro Cettolo | Germán Sanchis-Trilles | Marcello Federico
Proceedings of the 6th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper reports on the participation of FBK at the IWSLT 2009 Evaluation. This year we worked on the Arabic-English and Turkish-English BTEC tasks with a special effort on linguistic preprocessing techniques involving morphological segmentation. In addition, we investigated the adaptation problem in the development of systems for the Chinese-English and English-Chinese challenge tasks; in particular, we explored different ways for clustering training data into topic or dialog-specific subsets: by producing (and combining) smaller but more focused models, we intended to make better use of the available training data, with the ultimate purpose of improving translation quality.

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Online language model adaptation for spoken dialog translation
Germán Sanchis-Trilles | Mauro Cettolo | Nicola Bertoldi | Marcello Federico
Proceedings of the 6th International Workshop on Spoken Language Translation: Papers

This paper focuses on the problem of language model adaptation in the context of Chinese-English cross-lingual dialogs, as set-up by the challenge task of the IWSLT 2009 Evaluation Campaign. Mixtures of n-gram language models are investigated, which are obtained by clustering bilingual training data according to different available human annotations, respectively, at the dialog level, turn level, and dialog act level. For the latter case, clustering of IWSLT data was in fact induced through a comparable Italian-English parallel corpus provided with dialog act annotations. For the sake of adaptation, mixture weight estimation is performed either at the level of single source sentence or test set. Estimated weights are then transferred to the target language mixture model. Experimental results show that, by training different specific language models weighted according to the actual input instead of using a single target language model, significant gains in terms of perplexity and BLEU can be achieved.

2008

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Shallow-Syntax Phrase-Based Translation: Joint versus Factored String-to-Chunk Models
Mauro Cettolo | Marcello Federico | Daniele Pighin | Nicola Bertoldi
Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers

This work extends phrase-based statistical MT (SMT) with shallow syntax dependencies. Two string-to-chunks translation models are proposed: a factored model, which augments phrase-based SMT with layered dependencies, and a joint model, that extends the phrase translation table with microtags, i.e. per-word projections of chunk labels. Both rely on n-gram models of target sequences with different granularity: single words, micro-tags, chunks. In particular, n-grams defined over syntactic chunks should model syntactic constraints coping with word-group movements. Experimental analysis and evaluation conducted on two popular Chinese-English tasks suggest that the shallow-syntax joint-translation model has potential to outperform state-of-the-art phrase-based translation, with a reasonable computational overhead.

2007

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Efficient Handling of N-gram Language Models for Statistical Machine Translation
Marcello Federico | Mauro Cettolo
Proceedings of the Second Workshop on Statistical Machine Translation

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FBK@IWSLT 2007
Nicola Bertoldi | Mauro Cettolo | Roldano Cattoni | Marcello Federico
Proceedings of the Fourth International Workshop on Spoken Language Translation

This paper reports on the participation of FBK (formerly ITC-irst) at the IWSLT 2007 Evaluation. FBK participated in three tasks, namely Chinese-to-English, Japanese-to-English, and Italian-to-English. With respect to last year, translation systems were developed with the Moses Toolkit and the IRSTLM library, both available as open source software. Moreover, several novel ideas were investigated: the use of confusion networks in input to manage ambiguity in punctuation, the estimation of an additional language model by means of the Google’s Web 1T 5-gram collection, the combination of true case and lower case language models, and finally the use of multiple phrase-tables. By working on top of a state-of-the art baseline, experiments showed that the above methods accounted for significant BLEU score improvements.

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Better n-best translations through generative n-gram language models
Boxing Chen | Marcello Federico | Mauro Cettolo
Proceedings of Machine Translation Summit XI: Papers

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POS-based reordering models for statistical machine translation
Deepa Gupta | Mauro Cettolo | Marcello Federico
Proceedings of Machine Translation Summit XI: Papers

2006

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The ITC-irst SMT system for IWSLT 2006
Boxing Chen | Roldano Cattoni | Nicola Bertoldi | Mauro Cettolo | Marcello Federico
Proceedings of the Third International Workshop on Spoken Language Translation: Evaluation Campaign

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Reordering rules for phrase-based statistical machine translation
Boxing Chen | Mauro Cettolo | Marcello Federico
Proceedings of the Third International Workshop on Spoken Language Translation: Papers

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Maximum Entropy Tagging with Binary and Real-Valued Features
Vanessa Sandrini | Marcello Federico | Mauro Cettolo
Proceedings of the Workshop on Learning Structured Information in Natural Language Applications

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A Web-based Demonstrator of a Multi-lingual Phrase-based Translation System
Roldano Cattoni | Nicola Bertoldi | Mauro Cettolo | Boxing Chen | Marcello Federico
Demonstrations

2005

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The ITC-irst SMT System for IWSLT-2005
Boxing Chen | Roldano Cattoni | Nicola Bertoldi | Mauro Cettolo | Marcello Federico
Proceedings of the Second International Workshop on Spoken Language Translation

2004

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The ITC-irst statistical machine translation system for IWSLT-
Nicola Bertoldi | Roldano Cattoni | Mauro Cettolo | Marcello Federico
Proceedings of the First International Workshop on Spoken Language Translation: Evaluation Campaign

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Minimum error training of log-linear translation models
Mauro Cettolo | Marcello Federico
Proceedings of the First International Workshop on Spoken Language Translation: Papers