Josep M. Crego

Also published as: Josep Crego, Josep Maria Crego


2021

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Revisiting Multi-Domain Machine Translation
MinhQuang Pham | Josep Maria Crego | François Yvon
Transactions of the Association for Computational Linguistics, Volume 9

When building machine translation systems, one often needs to make the best out of heterogeneous sets of parallel data in training, and to robustly handle inputs from unexpected domains in testing. This multi-domain scenario has attracted a lot of recent work that fall under the general umbrella of transfer learning. In this study, we revisit multi-domain machine translation, with the aim to formulate the motivations for developing such systems and the associated expectations with respect to performance. Our experiments with a large sample of multi-domain systems show that most of these expectations are hardly met and suggest that further work is needed to better analyze the current behaviour of multi-domain systems and to make them fully hold their promises.

2020

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Integrating Domain Terminology into Neural Machine Translation
Elise Michon | Josep Crego | Jean Senellart
Proceedings of the 28th International Conference on Computational Linguistics

This paper extends existing work on terminology integration into Neural Machine Translation, a common industrial practice to dynamically adapt translation to a specific domain. Our method, based on the use of placeholders complemented with morphosyntactic annotation, efficiently taps into the ability of the neural network to deal with symbolic knowledge to surpass the surface generalization shown by alternative techniques. We compare our approach to state-of-the-art systems and benchmark them through a well-defined evaluation framework, focusing on actual application of terminology and not just on the overall performance. Results indicate the suitability of our method in the use-case where terminology is used in a system trained on generic data only.

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Boosting Neural Machine Translation with Similar Translations
Jitao Xu | Josep Crego | Jean Senellart
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

This paper explores data augmentation methods for training Neural Machine Translation to make use of similar translations, in a comparable way a human translator employs fuzzy matches. In particular, we show how we can simply present the neural model with information of both source and target sides of the fuzzy matches, we also extend the similarity to include semantically related translations retrieved using sentence distributed representations. We show that translations based on fuzzy matching provide the model with “copy” information while translations based on embedding similarities tend to extend the translation “context”. Results indicate that the effect from both similar sentences are adding up to further boost accuracy, combine naturally with model fine-tuning and are providing dynamic adaptation for unseen translation pairs. Tests on multiple data sets and domains show consistent accuracy improvements. To foster research around these techniques, we also release an Open-Source toolkit with efficient and flexible fuzzy-match implementation.

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Priming Neural Machine Translation
Minh Quang Pham | Jitao Xu | Josep Crego | François Yvon | Jean Senellart
Proceedings of the Fifth Conference on Machine Translation

Priming is a well known and studied psychology phenomenon based on the prior presentation of one stimulus (cue) to influence the processing of a response. In this paper, we propose a framework to mimic the process of priming in the context of neural machine translation (NMT). We evaluate the effect of using similar translations as priming cues on the NMT network. We propose a method to inject priming cues into the NMT network and compare our framework to other mechanisms that perform micro-adaptation during inference. Overall, experiments conducted in a multi-domain setting confirm that adding priming cues in the NMT decoder can go a long way towards improving the translation accuracy. Besides, we show the suitability of our framework to gather valuable information for an NMT network from monolingual resources.

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A Study of Residual Adapters for Multi-Domain Neural Machine Translation
Minh Quang Pham | Josep Maria Crego | François Yvon | Jean Senellart
Proceedings of the Fifth Conference on Machine Translation

Domain adaptation is an old and vexing problem for machine translation systems. The most common approach and successful to supervised adaptation is to fine-tune a baseline system with in-domain parallel data. Standard fine-tuning however modifies all the network parameters, which makes this approach computationally costly and prone to overfitting. A recent, lightweight approach, instead augments a baseline model with supplementary (small) adapter layers, keeping the rest of the mode unchanged. This has the additional merit to leave the baseline model intact, and adaptable to multiple domains. In this paper, we conduct a thorough analysis of the adapter model in the context of a multidomain machine translation task. We contrast multiple implementations of this idea on two language pairs. Our main conclusions are that residual adapters provide a fast and cheap method for supervised multi-domain adaptation; our two variants prove as effective as the original adapter model, and open perspective to also make adapted models more robust to label domain errors.

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Efficient and High-Quality Neural Machine Translation with OpenNMT
Guillaume Klein | Dakun Zhang | Clément Chouteau | Josep Crego | Jean Senellart
Proceedings of the Fourth Workshop on Neural Generation and Translation

This paper describes the OpenNMT submissions to the WNGT 2020 efficiency shared task. We explore training and acceleration of Transformer models with various sizes that are trained in a teacher-student setup. We also present a custom and optimized C++ inference engine that enables fast CPU and GPU decoding with few dependencies. By combining additional optimizations and parallelization techniques, we create small, efficient, and high-quality neural machine translation models.

2019

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SYSTRAN @ WAT 2019: Russian-Japanese News Commentary task
Jitao Xu | TuAnh Nguyen | MinhQuang Pham | Josep Crego | Jean Senellart
Proceedings of the 6th Workshop on Asian Translation

This paper describes Systran’s submissions to WAT 2019 Russian-Japanese News Commentary task. A challenging translation task due to the extremely low resources available and the distance of the language pair. We have used the neural Transformer architecture learned over the provided resources and we carried out synthetic data generation experiments which aim at alleviating the data scarcity problem. Results indicate the suitability of the data augmentation experiments, enabling our systems to rank first according to automatic evaluations.

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Enhanced Transformer Model for Data-to-Text Generation
Li Gong | Josep Crego | Jean Senellart
Proceedings of the 3rd Workshop on Neural Generation and Translation

Neural models have recently shown significant progress on data-to-text generation tasks in which descriptive texts are generated conditioned on database records. In this work, we present a new Transformer-based data-to-text generation model which learns content selection and summary generation in an end-to-end fashion. We introduce two extensions to the baseline transformer model: First, we modify the latent representation of the input, which helps to significantly improve the content correctness of the output summary; Second, we include an additional learning objective that accounts for content selection modelling. In addition, we propose two data augmentation methods that succeed to further improve performance of the resulting generation models. Evaluation experiments show that our final model outperforms current state-of-the-art systems as measured by different metrics: BLEU, content selection precision and content ordering. We made publicly available the transformer extension presented in this paper.

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SYSTRAN @ WNGT 2019: DGT Task
Li Gong | Josep Crego | Jean Senellart
Proceedings of the 3rd Workshop on Neural Generation and Translation

This paper describes SYSTRAN participation to the Document-level Generation and Trans- lation (DGT) Shared Task of the 3rd Workshop on Neural Generation and Translation (WNGT 2019). We participate for the first time using a Transformer network enhanced with modified input embeddings and optimising an additional objective function that considers content selection. The network takes in structured data of basketball games and outputs a summary of the game in natural language.

2018

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OpenNMT System Description for WNMT 2018: 800 words/sec on a single-core CPU
Jean Senellart | Dakun Zhang | Bo Wang | Guillaume Klein | Jean-Pierre Ramatchandirin | Josep Crego | Alexander Rush
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation

We present a system description of the OpenNMT Neural Machine Translation entry for the WNMT 2018 evaluation. In this work, we developed a heavily optimized NMT inference model targeting a high-performance CPU system. The final system uses a combination of four techniques, all of them lead to significant speed-ups in combination: (a) sequence distillation, (b) architecture modifications, (c) precomputation, particularly of vocabulary, and (d) CPU targeted quantization. This work achieves the fastest performance of the shared task, and led to the development of new features that have been integrated to OpenNMT and available to the community.

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Neural Network Architectures for Arabic Dialect Identification
Elise Michon | Minh Quang Pham | Josep Crego | Jean Senellart
Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

SYSTRAN competes this year for the first time to the DSL shared task, in the Arabic Dialect Identification subtask. We participate by training several Neural Network models showing that we can obtain competitive results despite the limited amount of training data available for learning. We report our experiments and detail the network architecture and parameters of our 3 runs: our best performing system consists in a Multi-Input CNN that learns separate embeddings for lexical, phonetic and acoustic input features (F1: 0.5289); we also built a CNN-biLSTM network aimed at capturing both spatial and sequential features directly from speech spectrograms (F1: 0.3894 at submission time, F1: 0.4235 with later found parameters); and finally a system relying on binary CNN-biLSTMs (F1: 0.4339).

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SYSTRAN Participation to the WMT2018 Shared Task on Parallel Corpus Filtering
MinhQuang Pham | Josep Crego | Jean Senellart
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This paper describes the participation of SYSTRAN to the shared task on parallel corpus filtering at the Third Conference on Machine Translation (WMT 2018). We participate for the first time using a neural sentence similarity classifier which aims at predicting the relatedness of sentence pairs in a multilingual context. The paper describes the main characteristics of our approach and discusses the results obtained on the data sets published for the shared task.

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Fixing Translation Divergences in Parallel Corpora for Neural MT
MinhQuang Pham | Josep Crego | Jean Senellart | François Yvon
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Corpus-based approaches to machine translation rely on the availability of clean parallel corpora. Such resources are scarce, and because of the automatic processes involved in their preparation, they are often noisy. This paper describes an unsupervised method for detecting translation divergences in parallel sentences. We rely on a neural network that computes cross-lingual sentence similarity scores, which are then used to effectively filter out divergent translations. Furthermore, similarity scores predicted by the network are used to identify and fix some partial divergences, yielding additional parallel segments. We evaluate these methods for English-French and English-German machine translation tasks, and show that using filtered/corrected corpora actually improves MT performance.

2017

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SYSTRAN Purely Neural MT Engines for WMT2017
Yongchao Deng | Jungi Kim | Guillaume Klein | Catherine Kobus | Natalia Segal | Christophe Servan | Bo Wang | Dakun Zhang | Josep Crego | Jean Senellart
Proceedings of the Second Conference on Machine Translation

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Domain Control for Neural Machine Translation
Catherine Kobus | Josep Crego | Jean Senellart
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

Machine translation systems are very sensitive to the domains they were trained on. Several domain adaptation techniques have already been deeply studied. We propose a new technique for neural machine translation (NMT) that we call domain control which is performed at runtime using a unique neural network covering multiple domains. The presented approach shows quality improvements when compared to dedicated domains translating on any of the covered domains and even on out-of-domain data. In addition, model parameters do not need to be re-estimated for each domain, making this effective to real use cases. Evaluation is carried out on English-to-French translation for two different testing scenarios. We first consider the case where an end-user performs translations on a known domain. Secondly, we consider the scenario where the domain is not known and predicted at the sentence level before translating. Results show consistent accuracy improvements for both conditions.

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Boosting Neural Machine Translation
Dakun Zhang | Jungi Kim | Josep Crego | Jean Senellart
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Training efficiency is one of the main problems for Neural Machine Translation (NMT). Deep networks need for very large data as well as many training iterations to achieve state-of-the-art performance. This results in very high computation cost, slowing down research and industrialisation. In this paper, we propose to alleviate this problem with several training methods based on data boosting and bootstrap with no modifications to the neural network. It imitates the learning process of humans, which typically spend more time when learning “difficult” concepts than easier ones. We experiment on an English-French translation task showing accuracy improvements of up to 1.63 BLEU while saving 20% of training time.

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Adaptation incrémentale de modèles de traduction neuronaux (Incremental adaptation of neural machine translation models)
Christophe Servan | Josep Crego | Jean Senellart
Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts

L’adaptation au domaine est un verrou scientifique en traduction automatique. Il englobe généralement l’adaptation de la terminologie et du style, en particulier pour la post-édition humaine dans le cadre d’une traduction assistée par ordinateur. Avec la traduction automatique neuronale, nous étudions une nouvelle approche d’adaptation au domaine que nous appelons “spécialisation” et qui présente des résultats prometteurs tant dans la vitesse d’apprentissage que dans les scores de traduction. Dans cet article, nous proposons d’explorer cette approche.

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Conception d’une solution de détection d’événements basée sur Twitter (Design of a solution for event detection from Tweeter)
Christophe Servan | Catherine Kobus | Yongchao Deng | Cyril Touffet | Jungi Kim | Inès Kapp | Djamel Mostefa | Josep Crego | Aurélien Coquard | Jean Senellart
Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 3 - Démonstrations

Cet article présente un système d’alertes fondé sur la masse de données issues de Tweeter. L’objectif de l’outil est de surveiller l’actualité, autour de différents domaines témoin incluant les événements sportifs ou les catastrophes naturelles. Cette surveillance est transmise à l’utilisateur sous forme d’une interface web contenant la liste d’événements localisés sur une carte.

2012

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Joint WMT 2012 Submission of the QUAERO Project
Markus Freitag | Stephan Peitz | Matthias Huck | Hermann Ney | Jan Niehues | Teresa Herrmann | Alex Waibel | Hai-son Le | Thomas Lavergne | Alexandre Allauzen | Bianka Buschbeck | Josep Maria Crego | Jean Senellart
Proceedings of the Seventh Workshop on Statistical Machine Translation

2011

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LIMSI @ WMT11
Alexandre Allauzen | Hélène Bonneau-Maynard | Hai-Son Le | Aurélien Max | Guillaume Wisniewski | François Yvon | Gilles Adda | Josep Maria Crego | Adrien Lardilleux | Thomas Lavergne | Artem Sokolov
Proceedings of the Sixth Workshop on Statistical Machine Translation

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Joint WMT Submission of the QUAERO Project
Markus Freitag | Gregor Leusch | Joern Wuebker | Stephan Peitz | Hermann Ney | Teresa Herrmann | Jan Niehues | Alex Waibel | Alexandre Allauzen | Gilles Adda | Josep Maria Crego | Bianka Buschbeck | Tonio Wandmacher | Jean Senellart
Proceedings of the Sixth Workshop on Statistical Machine Translation

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From n-gram-based to CRF-based Translation Models
Thomas Lavergne | Alexandre Allauzen | Josep Maria Crego | François Yvon
Proceedings of the Sixth Workshop on Statistical Machine Translation

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Advances on spoken language translation in the Quaero program
Karim Boudahmane | Bianka Buschbeck | Eunah Cho | Josep Maria Crego | Markus Freitag | Thomas Lavergne | Hermann Ney | Jan Niehues | Stephan Peitz | Jean Senellart | Artem Sokolov | Alex Waibel | Tonio Wandmacher | Joern Wuebker | François Yvon
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign

The Quaero program is an international project promoting research and industrial innovation on technologies for automatic analysis and classification of multimedia and multilingual documents. Within the program framework, research organizations and industrial partners collaborate to develop prototypes of innovating applications and services for access and usage of multimedia data. One of the topics addressed is the translation of spoken language. Each year, a project-internal evaluation is conducted by DGA to monitor the technological advances. This work describes the design and results of the 2011 evaluation campaign. The participating partners were RWTH, KIT, LIMSI and SYSTRAN. Their approaches are compared on both ASR output and reference transcripts of speech data for the translation between French and German. The results show that the developed techniques further the state of the art and improve translation quality.

2010

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LIMSI’s Statistical Translation Systems for WMT’10
Alexandre Allauzen | Josep M. Crego | İlknur Durgar El-Kahlout | François Yvon
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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LIMSI @ IWSLT 2010
Alexandre Allauzen | Josep M. Crego | İlknur Durgar El-Kahlout | Le Hai-Son | Guillaume Wisniewski | François Yvon
Proceedings of the 7th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes LIMSI’s Statistical Machine Translation systems (SMT) for the IWSLT evaluation, where we participated in two tasks (Talk for English to French and BTEC for Turkish to English). For the Talk task, we studied an extension of our in-house n-code SMT system (the integration of a bilingual reordering model over generalized translation units), as well as the use of training data extracted from Wikipedia in order to adapt the target language model. For the BTEC task, we concentrated on pre-processing schemes on the Turkish side in order to reduce the morphological discrepancies with the English side. We also evaluated the use of two different continuous space language models for such a small size of training data.

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Multi-pivot translation by system combination
Gregor Leusch | Aurélien Max | Josep Maria Crego | Hermann Ney
Proceedings of the 7th International Workshop on Spoken Language Translation: Papers

This paper describes a technique to exploit multiple pivot languages when using machine translation (MT) on language pairs with scarce bilingual resources, or where no translation system for a language pair is available. The principal idea is to generate intermediate translations in several pivot languages, translate them separately into the target language, and generate a consensus translation out of these using MT system combination techniques. Our technique can also be applied when a translation system for a language pair is available, but is limited in its translation accuracy because of scarce resources. Using statistical MT systems for the 11 different languages of Europarl, we show experimentally that a direct translation system can be replaced by this pivot approach without a loss in translation quality if about six pivot languages are available. Furthermore, we can already improve an existing MT system by adding two pivot systems to it. The maximum improvement was found to be 1.4% abs. in BLEU in our experiments for 8 or more pivot languages.

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Contrastive Lexical Evaluation of Machine Translation
Aurélien Max | Josep Maria Crego | François Yvon
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper advocates a complementary measure of translation performance that focuses on the constrastive ability of two or more systems or system versions to adequately translate source words. This is motivated by three main reasons : 1) existing automatic metrics sometimes do not show significant differences that can be revealed by fine-grained focussed human evaluation, 2) these metrics are based on direct comparisons between system hypotheses with the corresponding reference translations, thus ignoring the input words that were actually translated, and 3) as these metrics do not take input hypotheses from several systems at once, fine-grained contrastive evaluation can only be done indirectly. This proposal is illustrated on a multi-source Machine Translation scenario where multiple translations of a source text are available. Significant gains (up to +1.3 BLEU point) are achieved on these experiments, and contrastive lexical evaluation is shown to provide new information that can help to better analyse a system's performance.

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Local lexical adaptation in Machine Translation through triangulation: SMT helping SMT
Josep Maria Crego | Aurélien Max | François Yvon
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Improving Reordering with Linguistically Informed Bilingual n-grams
Josep Maria Crego | François Yvon
Coling 2010: Posters

2009

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Gappy Translation Units under Left-to-Right SMT Decoding
Josep M. Crego | François Yvon
Proceedings of the 13th Annual conference of the European Association for Machine Translation

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Plusieurs langues (bien choisies) valent mieux qu’une : traduction statistique multi-source par renforcement lexical
Josep Maria Crego | Aurélien Max | François Yvon
Actes de la 16ème conférence sur le Traitement Automatique des Langues Naturelles. Articles courts

Les systèmes de traduction statistiques intègrent différents types de modèles dont les prédictions sont combinées, lors du décodage, afin de produire les meilleures traductions possibles. Traduire correctement des mots polysémiques, comme, par exemple, le mot avocat du français vers l’anglais (lawyer ou avocado), requiert l’utilisation de modèles supplémentaires, dont l’estimation et l’intégration s’avèrent complexes. Une alternative consiste à tirer parti de l’observation selon laquelle les ambiguïtés liées à la polysémie ne sont pas les mêmes selon les langues source considérées. Si l’on dispose, par exemple, d’une traduction vers l’espagnol dans laquelle avocat a été traduit par aguacate, alors la traduction de ce mot vers l’anglais n’est plus ambiguë. Ainsi, la connaissance d’une traduction français!espagnol permet de renforcer la sélection de la traduction avocado pour le système français!anglais. Dans cet article, nous proposons d’utiliser des documents en plusieurs langues pour renforcer les choix lexicaux effectués par un système de traduction automatique. En particulier, nous montrons une amélioration des performances sur plusieurs métriques lorsque les traductions auxiliaires utilisées sont obtenues manuellement.

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LIMSI‘s Statistical Translation Systems for WMT‘09
Alexandre Allauzen | Josep Crego | Aurélien Max | François Yvon
Proceedings of the Fourth Workshop on Statistical Machine Translation

2008

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Using Shallow Syntax Information to Improve Word Alignment and Reordering for SMT
Josep M. Crego | Nizar Habash
Proceedings of the Third Workshop on Statistical Machine Translation

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The TALP-UPC Ngram-Based Statistical Machine Translation System for ACL-WMT 2008
Maxim Khalilov | Adolfo Hernández H. | Marta R. Costa-jussà | Josep M. Crego | Carlos A. Henríquez Q. | Patrik Lambert | José A. R. Fonollosa | José B. Mariño | Rafael E. Banchs
Proceedings of the Third Workshop on Statistical Machine Translation

2007

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Discriminative Alignment Training without Annotated Data for Machine Translation
Patrik Lambert | Rafael E. Banchs | Josep M. Crego
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers

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Analysis and System Combination of Phrase- and N-Gram-Based Statistical Machine Translation Systems
Marta R. Costa-jussà | Josep M. Crego | David Vilar | José A. R. Fonollosa | José B. Mariño | Hermann Ney
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers

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Ngram-Based Statistical Machine Translation Enhanced with Multiple Weighted Reordering Hypotheses
Marta R. Costa-jussà | Josep M. Crego | Patrik Lambert | Maxim Khalilov | José A. R. Fonollosa | José B. Mariño | Rafael E. Banchs
Proceedings of the Second Workshop on Statistical Machine Translation

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Syntax-enhanced n-gram-based SMT
Josep M. Crego | José B. Mariño
Proceedings of Machine Translation Summit XI: Papers

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Extending MARIE: an N-gram-based SMT decoder
Josep M. Crego | José B. Mariño
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions

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The TALP ngram-based SMT system for IWSLT 2007
Patrik Lambert | Marta R. Costa-jussà | Josep M. Crego | Maxim Khalilov | José B. Mariño | Rafael E. Banchs | José A. R. Fonollosa | Holger Schwenk
Proceedings of the Fourth International Workshop on Spoken Language Translation

This paper describes TALPtuples, the 2007 N-gram-based statistical machine translation system developed at the TALP Research Center of the UPC (Universitat Polite`cnica de Catalunya) in Barcelona. Emphasis is put on improvements and extensions of the system of previous years. Mainly, these include optimizing alignment parameters in function of translation metric scores and rescoring with a neural network language model. Results on two translation directions are reported, namely from Arabic and Chinese into English, thoroughly explaining all language-related preprocessing and translation schemes.

2006

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N-gram-based Machine Translation
José Mariño | Rafael E. Banchs | Josep M. Crego | Adrià de Gispert | Patrik Lambert | José A. R. Fonollosa | Marta R. Costa-jussà
Computational Linguistics, Volume 32, Number 4, December 2006

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TALP Phrase-based statistical translation system for European language pairs
Marta R. Costa-jussà | Josep M. Crego | Adrià de Gispert | Patrik Lambert | Maxim Khalilov | José B. Mariño | José A. R. Fonollosa | Rafael Banchs
Proceedings on the Workshop on Statistical Machine Translation

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N-gram-based SMT System Enhanced with Reordering Patterns
Josep M. Crego | Adrià de Gispert | Patrik Lambert | Marta R. Costa-jussà | Maxim Khalilov | Rafael Banchs | José B. Mariño | José A. R. Fonollosa
Proceedings on the Workshop on Statistical Machine Translation

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Integration of POStag-based Source Reordering into SMT Decoding by an Extended Search Graph
Josep M. Crego | José B. Mariño
Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers

This paper presents a reordering framework for statistical machine translation (SMT) where source-side reorderings are integrated into SMT decoding, allowing for a highly constrained reordered search graph. The monotone search is extended by means of a set of reordering patterns (linguistically motivated rewrite patterns). Patterns are automatically learnt in training from word-to-word alignments and source-side Part-Of-Speech (POS) tags. Traversing the extended search graph, the decoder evaluates every hypothesis making use of a group of widely used SMT models and helped by an additional Ngram language model of source-side POS tags. Experiments are reported on the Euparl task (Spanish-to-English and English-to- Spanish). Results are presented regarding translation accuracy (using human and automatic evaluations) and computational efficiency, showing significant improvements in translation quality for both translation directions at a very low computational cost.

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The TALP Ngram-based SMT systems for IWSLT 2006
Josep M. Crego | Adrià de Gispert | Patrick Lambert | Maxim Khalilov | Marta R. Costa-jussà | José B. Mariño | Rafael Banchs | José A. R. Fonollosa
Proceedings of the Third International Workshop on Spoken Language Translation: Evaluation Campaign

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TALP phrase-based system and TALP system combination for IWSLT 2006
Marta R. Costa-jussà | Josep M. Crego | Adrià de Gispert | Patrik Lambert | Maxim Khalilov | José A. R. Fonollosa | José B. Mariño | Rafael Banchs
Proceedings of the Third International Workshop on Spoken Language Translation: Evaluation Campaign

2005

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Ngram-based versus Phrase-based Statistical Machine Translation
Josep M. Crego | Marta R. Costa-Jussa | Jose B. Marino | Jose A. R. Fonollosa
Proceedings of the Second International Workshop on Spoken Language Translation

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The TALP Ngram-based SMT System for IWSLT’05
Josep M. Crego | Adria de Gispert | Jose B. Marino
Proceedings of the Second International Workshop on Spoken Language Translation

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Bilingual N-gram Statistical Machine Translation
José B. Mariño | Rafael E. Banchs | Josep M. Crego | Adrià de Gispert | Patrik Lambert | José A. R. Fonollosa | Marta Ruiz
Proceedings of Machine Translation Summit X: Papers

This paper describes a statistical machine translation system that uses a translation model which is based on bilingual n-grams. When this translation model is log-linearly combined with four specific feature functions, state of the art translations are achieved for Spanish-to-English and English-to-Spanish translation tasks. Some specific results obtained for the EPPS (European Parliament Plenary Sessions) data are presented and discussed. Finally, future research issues are depicted.

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Reordered Search, and Tuple Unfolding for Ngram-based SMT
Josep M. Crego | José B. Mariño | Adrià de Gispert
Proceedings of Machine Translation Summit X: Papers

In Statistical Machine Translation, the use of reordering for certain language pairs can produce a significant improvement on translation accuracy. However, the search problem is shown to be NP-hard when arbitrary reorderings are allowed. This paper addresses the question of reordering for an Ngram-based SMT approach following two complementary strategies, namely reordered search and tuple unfolding. These strategies interact to improve translation quality in a Chinese to English task. On the one hand, we allow for an Ngram-based decoder (MARIE) to perform a reordered search over the source sentence, while combining a translation tuples Ngram model, a target language model, a word penalty and a word distance model. Interestingly, even though the translation units are learnt sequentially, its reordered search produces an improved translation. On the other hand, we allow for a modification of the translation units that unfolds the tuples, so that shorter units are learnt from a new parallel corpus, where the source sentences are reordered according to the target language. This tuple unfolding technique reduces data sparseness and, when combined with the reordered search, further boosts translation performance. Translation accuracy and efficency results are reported for the IWSLT 2004 Chinese to English task.

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Statistical Machine Translation of Euparl Data by using Bilingual N-grams
Rafael E. Banchs | Josep M. Crego | Adrià de Gispert | Patrik Lambert | José B. Mariño
Proceedings of the ACL Workshop on Building and Using Parallel Texts

2004

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Bilingual Connections for Trilingual Corpora: An XML Approach
Victoria Arranz | Núria Castell | Josep Maria Crego | Jesús Giménez | Adrià de Gispert | Patrik Lambert
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Phrase-based alignment combining corpus cooccurrences and linguistic knowledge
Adria de Gispert | Jose B. Marino | Josep M. Crego
Proceedings of the First International Workshop on Spoken Language Translation: Papers