International Conference on Spoken Language Translation (2010)



up

bib (full) Proceedings of the 7th International Workshop on Spoken Language Translation: Evaluation Campaign

pdf bib
Proceedings of the 7th International Workshop on Spoken Language Translation: Evaluation Campaign

pdf bib
Overview of the IWSLT 2010 evaluation campaign
Michael Paul | Marcello Federico | Sebastian Stüker

This paper gives an overview of the evaluation campaign results of the 7th International Workshop on Spoken Language Translation (IWSLT 2010)1. This year, we focused on three spoken language tasks: (1) public speeches on a variety of topics (TALK) from English to French, (2) spoken dialog in travel situations (DIALOG) between Chinese and English, and (3) traveling expressions (BTEC) from Arabic, Turkish, and French to English. In total, 28 teams (including 7 firsttime participants) took part in the shared tasks, submitting 60 primary and 112 contrastive runs. Automatic and subjective evaluations of the primary runs were carried out in order to investigate the impact of different communication modalities, spoken language styles and semantic context on automatic speech recognition (ASR) and machine translation (MT) system performances.

pdf bib
AppTek’s APT machine translation system for IWSLT 2010
Evgeny Matusov | Selçuk Köprü

In this paper, we describe AppTek’s new APT machine translation system that we employed in the IWSLT 2010 evaluation campaign. This year, we participated in the Arabic-to-English and Turkish-to-English BTEC tasks. We discuss the architecture of the system, the preprocessing steps and the experiments carried out during the campaign. We show that competitive translation quality can be obtained with a system that can be turned into a real-life product without much effort.

pdf
The DCU machine translation systems for IWSLT 2010
Hala Almaghout | Jie Jiang | Andy Way

pdf
N-gram-based machine translation enhanced with neural networks
Francisco Zamora-Martinez | Maria Jose Castro-Bleda | Holger Schwenk

pdf
FBK @ IWSLT 2010
Arianna Bisazza | Ioannis Klasinas | Mauro Cettolo | Marcello Federico

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.

pdf
The GREYC/LLACAN machine translation systems for the IWSLT 2010 campaign
Julien Gosme | Wigdan Mekki | Fathi Debili | Yves Lepage | Nadine Lucas

In this paper we explore the contribution of the use of two Arabic morphological analyzers as preprocessing tools for statistical machine translation. Similar investigations have already been reported for morphologically rich languages like German, Turkish and Arabic. Here, we focus on the case of the Arabic language and mainly discuss the use of the G-LexAr analyzer. A preliminary experiment has been designed to choose the most promising translation system among the 3 G-LexAr-based systems, we concluded that the systems are equivalent. Nevertheless, we decided to use the lemmatized output of G-LexAr and use its translations as primary run for the BTEC AE track. The results showed that G-LexAr outputs degrades translation compared to the basic SMT system trained on the un-analyzed corpus.

pdf
I2R’s machine translation system for IWSLT 2010
Xiangyu Duan | Rafael Banchs | Jun Lang | Deyi Xiong | Aiti Aw | Min Zhang | Haizhou Li

pdf
The ICT statistical machine translation system for IWSLT 2010
Hao Xiong | Jun Xie | Hui Yu | Kai Liu | Wei Luo | Haitao Mi | Yang Liu | Yajuan Lü | Qun Liu

pdf
The INESC-ID machine translation system for the IWSLT 2010
Wang Ling | Tiago Luís | João Graça | Luísa Coheur | Isabel Trancoso

In this paper we describe the Instituto de Engenharia de Sistemas e Computadores Investigac ̧a ̃o e Desenvolvimento (INESC-ID) system that participated in the IWSLT 2010 evaluation campaign. Our main goal for this evaluation was to employ several state-of-the-art methods applied to phrase-based machine translation in order to improve the translation quality. Aside from the IBM M4 alignment model, two constrained alignment models were tested, which produced better overall results. These results were further improved by using weighted alignment matrixes during phrase extraction, rather than the single best alignment. Finally, we tested several filters that ruled out phrase pairs based on puntuation. Our system was evaluated on the BTEC and DIALOG tasks, having achieved a better overall ranking in the DIALOG task.

pdf
ITI-UPV machine translation system for IWSLT 2010
Guillem Gascó | Vicent Alabau | Jesús-Andrés Ferrer | Jesús González-Rubio | Martha-Alicia Rocha | Germán Sanchis-Trilles | Francisco Casacuberta | Jorge González | Joan-Andreu Sánchez

This paper presents the submissions of the PRHLT group for the evaluation campaign of the International Workshop on Spoken Language Translation. We focus on the development of reliable translation systems between syntactically different languages (DIALOG task) and on the efficient training of SMT models in resource-rich scenarios (TALK task).

pdf
The KIT translation system for IWSLT 2010
Jan Niehues | Mohammed Mediani | Teresa Herrmann | Michael Heck | Christian Herff | Alex Waibel

pdf
LIG statistical machine translation systems for IWSLT 2010
Laurent Besacier | Haitem Afli | Thi Ngoc Diep Do | Hervé Blanchon | Marion Potet

pdf
LIMSI @ IWSLT 2010
Alexandre Allauzen | Josep M. Crego | İlknur Durgar El-Kahlout | Le Hai-Son | Guillaume Wisniewski | François Yvon

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.

pdf
LIUM’s statistical machine translation system for IWSLT 2010
Anthony Rousseau | Loïc Barrault | Paul Deléglise | Yannick Estève

This paper describes the two systems developed by the LIUM laboratory for the 2010 IWSLT evaluation campaign. We participated to the new English to French TALK task. We developed two systems, one for each evaluation condition, both being statistical phrase-based systems using the the Moses toolkit. Several approaches were investigated.

pdf
The MIRACL Arabic-English statistical machine translation system for IWSLT 2010
Ines Turki Khemakhem | Salma Jamoussi | Abdelmajid Ben Hamadou

This paper describes the MIRACL statistical Machine Translation system and the improvements that were developed during the IWSLT 2010 evaluation campaign. We participated to the Arabic to English BTEC tasks using a phrase-based statistical machine translation approach. In this paper, we first discuss some challenges in translating from Arabic to English and we explore various techniques to improve performances on a such task. Next, we present our solution for disambiguating the output of an Arabic morphological analyzer. In fact, The Arabic morphological analyzer used produces all possible morphological structures for each word, with an unique correct proposition. In this work we exploit the Arabic-English alignment to choose the correct segmented form and the correct morpho-syntactic features produced by our morphological analyzer.

pdf
The MIT-LL/AFRL IWSLT-2010 MT system
Wade Shen | Timothy Anderson | Raymond Slyh | A. Ryan Aminzadeh

This paper describes the MIT-LL/AFRL statistical MT system and the improvements that were developed during the IWSLT 2010 evaluation campaign. As part of these efforts, we experimented with a number of extensions to the standard phrase-based model that improve performance on the Arabic and Turkish to English translation tasks. We also participated in the new French to English BTEC and English to French TALK tasks. We discuss the architecture of the MIT-LL/AFRL MT system, improvements over our 2008 system, and experiments we ran during the IWSLT-2010 evaluation. Specifically, we focus on 1) cross-domain translation using MAP adaptation, 2) Turkish morphological processing and translation, 3) improved Arabic morphology for MT preprocessing, and 4) system combination methods for machine translation.

pdf
The MSRA machine translation system for IWSLT 2010
Chi-Ho Li | Nan Duan | Yinggong Zhao | Shujie Liu | Lei Cui | Mei-yuh Hwang | Amittai Axelrod | Jianfeng Gao | Yaodong Zhang | Li Deng

pdf
The NICT translation system for IWSLT 2010
Chooi-Ling Goh | Taro Watanabe | Michael Paul | Andrew Finch | Eiichiro Sumita

This paper describes NICT’s participation in the IWSLT 2010 evaluation campaign for the DIALOG translation (Chinese-English) and the BTEC (French-English) translation shared-tasks. For the DIALOG translation, the main challenge to this task is applying context information during translation. Context information can be used to decide on word choice and also to replace missing information during translation. We applied discriminative reranking using contextual information as additional features. In order to provide more choices for re-ranking, we generated n-best lists from multiple phrase-based statistical machine translation systems that varied in the type of Chinese word segmentation schemes used. We also built a model that merged the phrase tables generated by the different segmentation schemes. Furthermore, we used a lattice-based system combination model to combine the output from different systems. A combination of all of these systems was used to produce the n-best lists for re-ranking. For the BTEC task, a general approach that used latticebased system combination of two systems, a standard phrasebased system and a hierarchical phrase-based system, was taken. We also tried to process some unknown words by replacing them with the same words but different inflections that are known to the system.

pdf
NTT statistical MT system for IWSLT 2010
Katsuhito Sudoh | Kevin Duh | Hajime Tsukada

pdf
The POSTECH’s statistical machine translation system for the IWSLT 2010
Hwidong Na | Jong-Hyeok Lee

pdf
The QMUL system description for IWSLT 2010
Sirvan Yahyaei | Christof Monz

pdf
The RWTH Aachen machine translation system for IWSLT 2010
Saab Mansour | Stephan Peitz | David Vilar | Joern Wuebker | Hermann Ney

In this paper we describe the statistical machine translation system of the RWTH Aachen University developed for the translation task of the IWSLT 2010. This year, we participated in the BTEC translation task for the Arabic to English language direction. We experimented with two state-of-theart decoders: phrase-based and hierarchical-based decoders. Extensions to the decoders included phrase training (as opposed to heuristic phrase extraction) for the phrase-based decoder, and soft syntactic features for the hierarchical decoder. Additionally, we experimented with various rule-based and statistical-based segmenters for Arabic. Due to the different decoders and the different methodologies that we apply for segmentation, we expect that there will be complimentary variation in the results achieved by each system. The next step would be to exploit these variations and achieve better results by combining the systems. We try different strategies for system combination and report significant improvements over the best single system.

pdf
Tel Aviv University’s system description for IWSLT 2010
Kfir Bar | Nachum Dershowitz

pdf
Statistical pattern-based MT with statistical French-English MT
Jin’ichi Murakami | Takuya Nishimura | Masao Tokuhisa

pdf
The TÜBİTAK-UEKAE statistical machine translation system for IWSLT 2010
Coskun Mermer | Hamza Kaya | Mehmet Uğur Doğan

We report on our participation in the IWSLT 2010 evaluation campaign. Similar to previous years, our submitted systems are based on the Moses statistical machine translation toolkit. This year, we also experimented with hierarchical phrase-based models. In addition, we utilized automatic minimum error-rate training instead of manually-guided tuning. We focused more on the BTEC Turkish-English task and explored various experimentations with unsupervised segmentation to measure their effects on the translation performance. We present the results of several contrastive experiments, including those that failed to improve the translation performance.

pdf
UPC-BMIC-VDU system description for the IWSLT 2010: testing several collocation segmentations in a phrase-based SMT system
Carlos Henríquez | Marta R. Costa-jussà | Vidas Daudaravicius | Rafael E. Banchs | José B. Mariño

This paper describes the UPC-BMIC-VMU participation in the IWSLT 2010 evaluation campaign. The SMT system is a standard phrase-based enriched with novel segmentations. These novel segmentations are computed using statistical measures such as Log-likelihood, T-score, Chi-squared, Dice, Mutual Information or Gravity-Counts. The analysis of translation results allows to divide measures into three groups. First, Log-likelihood, Chi-squared and T-score tend to combine high frequency words and collocation segments are very short. They improve the SMT system by adding new translation units. Second, Mutual Information and Dice tend to combine low frequency words and collocation segments are short. They improve the SMT system by smoothing the translation units. And third, GravityCounts tends to combine high and low frequency words and collocation segments are long. However, in this case, the SMT system is not improved. Thus, the road-map for translation system improvement is to introduce new phrases with either low frequency or high frequency words. It is hard to introduce new phrases with low and high frequency words in order to improve translation quality. Experimental results are reported in the French-to-English IWSLT 2010 evaluation where our system was ranked 3rd out of nine systems.

pdf
ILLC-UvA machine translation system for the IWSLT 2010 evaluation
Maxim Khalilov | Khalil Sima’an

pdf
The UvA system description for IWSLT 2010
Spyros Martzoukos | Christof Monz


up

bib (full) Proceedings of the 7th International Workshop on Spoken Language Translation: Papers

pdf bib
CCG augmented hierarchical phrase-based machine translation
Hala Almaghout | Jie Jiang | Andy Way

pdf bib
An algorithm for cross-lingual sense-clustering tested in a MT evaluation setting
Marianna Apidianaki | Yifan He

pdf bib
Mining parallel fragments from comparable texts
Mauro Cettolo | Marcello Federico | Nicola Bertoldi

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.

pdf
Improved Vietnamese-French parallel corpus mining using English language
Thi Ngoc Diep Do | Laurent Besacier | Eric Castelli

pdf
Analysis of translation model adaptation in statistical machine translation
Kevin Duh | Katsuhito Sudoh | Hajime Tsukada

pdf
The pay-offs of preprocessing for German-English statistical machine translation
Ilknur Durgar El-Kahlout | Francois Yvon

In this paper, we present the result of our work on improving the preprocessing for German-English statistical machine translation. We implemented and tested various improvements aimed at i) converting German texts to the new orthographic conventions; ii) performing a new tokenization for German; iii) normalizing lexical redundancy with the help of POS tagging and morphological analysis; iv) splitting German compound words with frequency based algorithm and; v) reducing singletons and out-of-vocabulary words. All these steps are performed during preprocessing on the German side. Combining all these processes, we reduced 10% of the singletons, 2% OOV words, and obtained 1.5 absolute (7% relative) BLEU improvement on the WMT 2010 German to English News translation task.

pdf
A Bayesian model of bilingual segmentation for transliteration
Andrew Finch | Eiichiro Sumita

pdf
Faster cube pruning
Andrea Gesmundo | James Henderson

pdf
Factor templates for factored machine translation models
Yvette Graham | Josef van Genabith

pdf
Modelling pronominal anaphora in statistical machine translation
Christian Hardmeier | Marcello Federico

Current Statistical Machine Translation (SMT) systems translate texts sentence by sentence without considering any cross-sentential context. Assuming independence between sentences makes it difficult to take certain translation decisions when the necessary information cannot be determined locally. We argue for the necessity to include crosssentence dependencies in SMT. As a case in point, we study the problem of pronominal anaphora translation by manually evaluating German-English SMT output. We then present a word dependency model for SMT, which can represent links between word pairs in the same or in different sentences. We use this model to integrate the output of a coreference resolution system into English-German SMT with a view to improving the translation of anaphoric pronouns.

pdf
A combination of hierarchical systems with forced alignments from phrase-based systems
Carmen Heger | Joern Wuebker | David Vilar | Hermann Ney

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

pdf
Multi-pivot translation by system combination
Gregor Leusch | Aurélien Max | Josep Maria Crego | Hermann Ney

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.

pdf
Real-time spoken language identification and recognition for speech-to-speech translation
Daniel Chung Yong Lim | Ian Lane | Alex Waibel

pdf
Towards a general and extensible phrase-extraction algorithm
Wang Ling | Tiago Luís | João Graça | Luísa Coheur | Isabel Trancoso

Phrase-based systems deeply depend on the quality of their phrase tables and therefore, the process of phrase extraction is always a fundamental step. In this paper we present a general and extensible phrase extraction algorithm, where we have highlighted several control points. The instantiation of these control points allows the simulation of previous approaches, as in each one of these points different strategies/heuristics can be tested. We show how previous approaches fit in this algorithm, compare several of them and, in addition, we propose alternative heuristics, showing their impact on the final translation results. Considering two different test scenarios from the IWSLT 2010 competition (BTEC, Fr-En and DIALOG, Cn-En), we have obtained an improvement in the results of 2.4 and 2.8 BLEU points, respectively.

pdf
MorphTagger: HMM-based Arabic segmentation for statistical machine translation
Saab Mansour

In this paper, we investigate different methodologies of Arabic segmentation for statistical machine translation by comparing a rule-based segmenter to different statistically-based segmenters. We also present a new method for segmentation that serves the need for a real-time translation system without impairing the translation accuracy.

pdf
Comparing intrinsic and extrinsic evaluation of MT output in a dialogue system
Anne H. Schneider | Ielka van der Sluis | Saturnino Luz

pdf
Sign language machine translation overkill
Daniel Stein | Christoph Schmidt | Hermann Ney

Sign languages represent an interesting niche for statistical machine translation that is typically hampered by the scarceness of suitable data, and most papers in this area apply only a few, well-known techniques and do not adapt them to small-sized corpora. In this paper, we will propose new methods for common approaches like scaling factor optimization and alignment merging strategies which helped improve our baseline. We also conduct experiments with different decoders and employ state-of-the-art techniques like soft syntactic labels as well as trigger-based and discriminative word lexica and system combination. All methods are evaluated on one of the largest sign language corpora available.

pdf
If I only had a parser: poor man’s syntax for hierarchical machine translation
David Vilar | Daniel Stein | Stephan Peitz | Hermann Ney

pdf
Dynamic distortion in a discriminative reordering model for statistical machine translation
Sirvan Yahyaei | Christoph Monz