This paper gives an overview of the evaluation campaign results of the International1Workshop on Spoken Language Translation (IWSLT) 2008 . In this workshop, we focused on the translation of spontaneous speech recorded in a real situation and the feasability of pivot-language-based translation approaches. The translation directions were English into Chinese and vice versa for the Challenge Task, Chinese into English and English into Spanish for the Pivot Task, and Arabic, Chinese, Spanish into English for the standard BTEC Task. In total, 19 research groups building 58 MT engines participated in this year’s event. Automatic and subjective evaluations were carried out in order to investigate the impact of spontaneity aspects of field data experiments on automatic speech recognition (ASR) and machine translation (MT) system performance as well as the robustness of state-of-the-art MT systems towards speech-to-speech translation in real environments.

We present the CMU Syntax Augmented Machine Translation System that was used in the IWSLT-08 evaluation campaign. We participated in the Full-BTEC data track for Chinese-English translation, focusing on transcript translation. For this year’s evaluation, we ported the Syntax Augmented MT toolkit [1] to the Hadoop MapReduce [2] parallel processing architecture, allowing us to efficiently run experiments evaluating a novel “wider pipelines” approach to integrate evidence from N -best alignments into our translation models. We describe each step of the MapReduce pipeline as it is implemented in the open-source SAMT toolkit, and show improvements in translation quality by using N-best alignments in both hierarchical and syntax augmented translation systems.

In this paper, we give a description of the machine translation (MT) system developed at DCU that was used for our third participation in the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT 2008). In this participation, we focus on various techniques for word and phrase alignment to improve system quality. Specifically, we try out our word packing and syntax-enhanced word alignment techniques for the Chinese–English task and for the English–Chinese task for the first time. For all translation tasks except Arabic–English, we exploit linguistically motivated bilingual phrase pairs extracted from parallel treebanks. We smooth our translation tables with out-of-domain word translations for the Arabic–English and Chinese–English tasks in order to solve the problem of the high number of out of vocabulary items. We also carried out experiments combining both in-domain and out-of-domain data to improve system performance and, finally, we deploy a majority voting procedure combining a language model-based method and a translation-based method for case and punctuation restoration. We participated in all the translation tasks and translated both the single-best ASR hypotheses and the correct recognition results. The translation results confirm that our new word and phrase alignment techniques are often helpful in improving translation quality, and the data combination method we proposed can significantly improve system performance.

This paper reports on the participation of FBK at the IWSLT 2008 Evaluation. Main effort has been spent on the Chinese-Spanish Pivot task. We implemented four methods to perform pivot translation. The results on the IWSLT 2008 test data show that our original method for generating training data through random sampling outperforms the best methods based on coupling translation systems. FBK also participated in the Chinese-English Challenge task and the Chinese-English and Chinese-Spanish BTEC tasks, employing the standard state-of-the-art MT system Moses Toolkit.

This year's GREYC machine translation (MT) system presents three major changes relative to the system presented during the previous campaign, while, of course, remaining a pure example-based MT system that exploits proportional analogies. Firstly, the analogy solver has been replaced with a truly non-deterministic one. Secondly, the engine has been re-engineered and a better control has been introduced. Thirdly, the data used for translation were the data provided by the organizers plus alignments obtained using a new alignment method. This year we chose to have the engine run with the word as the processing unit on the contrary to previous years where the processing unit used to be the character. The tracks the system participated in are all classic BTEC tracks (Arabic-English, Chinese-English and Chinese-Spanish) plus the so-called PIVOT task, where the test set had to be translated from Chinese into Spanish by way of English.

In this paper, we describe the system and approach used by the Institute for Infocomm Research (I2R) for the IWSLT 2008 spoken language translation evaluation campaign. In the system, we integrate various decoding algorithms into a multi-pass translation framework. The multi-pass approach enables us to utilize various decoding algorithm and to explore much more hypotheses. This paper reports our design philosophy, overall architecture, each individual system and various system combination methods that we have explored. The performance on development and test sets are reported in detail in the paper. The system has shown competitive performance with respect to the BLEU and METEOR measures in Chinese-English Challenge and BTEC tasks.

This paper presents a description for the ICT systems involved in the IWSLT 2008 evaluation campaign. This year, we participated in Chinese-English and English-Chinese translation directions. Four statistical machine translation systems were used: one linguistically syntax-based, two formally syntax-based, and one phrase-based. The outputs of the four SMT systems were fed to a sentence-level system combiner, which was expected to produce better translations than single systems. We will report the results of the four single systems and the combiner on both the development and test sets.

This paper is a description of the system presented by the LIG laboratory to the IWSLT08 speech translation evaluation. The LIG participated, for the second time this year, in the Arabic to English speech translation task. For translation, we used a conventional statistical phrase-based system developed using the moses open source decoder. We describe chronologically the improvements made since last year, starting from the IWSLT 2007 system, following with the improvements made for our 2008 submission. Then, we discuss in section 5 some post-evaluation experiments made very recently, as well as some on-going work on Arabic / English speech to text translation. This year, the systems were ranked according to the (BLEU+METEOR)/2 score of the primary ASR output run submissions. The LIG was ranked 5th/10 based on this rule.

This paper describes the system developed by the LIUM laboratory for the 2008 IWSLT evaluation. We only participated in the Arabic/English BTEC task. We developed a statistical phrase-based system using the Moses toolkit and SYSTRAN’s rule-based translation system to perform a morphological decomposition of the Arabic words. A continuous space language model was deployed to improve the modeling of the target language. Both approaches achieved significant improvements in the BLEU score. The system achieves a score of 49.4 on the test set of the 2008 IWSLT evaluation.

This paper describes the MIT-LL/AFRL statistical MT system and the improvements that were developed during the IWSLT 2008 evaluation campaign. As part of these efforts, we experimented with a number of extensions to the standard phrase-based model that improve performance for both text and speech-based translation on Chinese and Arabic translation tasks. We discuss the architecture of the MIT-LL/AFRL MT system, improvements over our 2007 system, and experiments we ran during the IWSLT-2008 evaluation. Specifically, we focus on 1) novel segmentation models for phrase-based MT, 2) improved lattice and confusion network decoding of speech input, 3) improved Arabic morphology for MT preprocessing, and 4) system combination methods for machine translation.

This paper describes the National Institute of Information and Communications Technology/Advanced Telecommunications Research Institute International (NICT/ATR) statistical machine translation (SMT) system used for the IWSLT 2008 evaluation campaign. We participated in the Chinese–English (Challenge Task), English–Chinese (Challenge Task), Chinese–English (BTEC Task), Chinese–Spanish (BTEC Task), and Chinese–English–Spanish (PIVOT Task) translation tasks. In the English–Chinese translation Challenge Task, we focused on exploring various factors for the English–Chinese translation because the research on the translation of English–Chinese is scarce compared to the opposite direction. In the Chinese–English translation Challenge Task, we employed a novel clustering method, where training sentences similar to the development data in terms of the word error rate formed a cluster. In the pivot translation task, we integrated two strategies for pivot translation by linear interpolation.

This paper describes our statistical machine translation system (CASIA) used in the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2008. In this year's evaluation, we participated in challenge task for Chinese-English and English-Chinese, BTEC task for Chinese-English. Here, we mainly introduce the overview of our system, the primary modules, the key techniques, and the evaluation results.

The NTT Statistical Machine Translation System consists of two primary components: a statistical machine translation decoder and a reranker. The decoder generates k-best translation canditates using a hierarchical phrase-based translation based on synchronous context-free grammar. The decoder employs a linear feature combination among several real-valued scores on translation and language models. The reranker reorders the k-best translation candidates using Ranking SVMs with a large number of sparse features. This paper describes the two components and presents the results for the evaluation campaign of IWSLT 2008.

In this paper, we describe POSTECH system for IWSLT 2008 evaluation campaign. The system is based on phrase based statistical machine translation. We set up a baseline system using well known freely available software. A preprocessing method and a language modeling method have been applied to the baseline system in order to improve machine translation quality. The preprocessing method is to identify and remove useless tokens in source texts. And the language modeling method models phrase level n-gram. We have participated in the BTEC tasks to see the effects of our methods.

The QMUL system to the IWSLT 2008 evaluation campaign is a phrase-based statistical MT system implemented in C++. The decoder employs a multi-stack architecture, and uses a beam to manage the search space. We participated in both BTEC Arabic → English and Chinese → English tracks, as well as the PIVOT task. In our first submission to IWSLT, we are particularly interested in seeing how our SMT system performs with speech input, having so far only worked with and translated newswire data sets.

RWTH’s system for the 2008 IWSLT evaluation consists of a combination of different phrase-based and hierarchical statistical machine translation systems. We participated in the translation tasks for the Chinese-to-English and Arabic-to-English language pairs. We investigated different preprocessing techniques, reordering methods for the phrase-based system, including reordering of speech lattices, and syntax-based enhancements for the hierarchical systems. We also tried the combination of the Arabic-to-English and Chinese-to-English outputs as an additional submission.

This paper gives a description of the statistical machine translation (SMT) systems developed at the TALP Research Center of the UPC (Universitat Polite`cnica de Catalunya) for our participation in the IWSLT’08 evaluation campaign. We present Ngram-based (TALPtuples) and phrase-based (TALPphrases) SMT systems. The paper explains the 2008 systems’ architecture and outlines translation schemes we have used, mainly focusing on the new techniques that are challenged to improve speech-to-speech translation quality. The novelties we have introduced are: improved reordering method, linear combination of translation and reordering models and new technique dealing with punctuation marks insertion for a phrase-based SMT system. This year we focus on the Arabic-English, Chinese-Spanish and pivot Chinese-(English)-Spanish translation tasks.

This paper reports on the first participation of TCH (Toshiba (China) Research and Development Center) at the IWSLT evaluation campaign. We participated in all the 5 translation tasks with Chinese as source language or target language. For Chinese-English and English-Chinese translation, we used hybrid systems that combine rule-based machine translation (RBMT) method and statistical machine translation (SMT) method. For Chinese-Spanish translation, phrase-based SMT models were used. For the pivot task, we combined the translations generated by a pivot based statistical translation model and a statistical transfer translation model (firstly, translating from Chinese to English, and then from English to Spanish). Moreover, for better performance of MT, we improved each module in the MT systems as follows: adapting Chinese word segmentation to spoken language translation, selecting out-of-domain corpus to build language models, using bilingual dictionaries to correct word alignment results, handling NE translation and selecting translations from the outputs of multiple systems. According to the automatic evaluation results on the full test sets, we top in all the 5 tasks.

In this study, we paid attention to the reliability of phrase table. We have been used the phrase table using Och’s method[2]. And this method sometimes generate completely wrong phrase tables. We found that such phrase table caused by long parallel sentences. Therefore, we removed these long parallel sentences from training data. Also, we utilized general tools for statistical machine translation, such as ”Giza++”[3], ”moses”[4], and ”training-phrase-model.perl”[5]. We obtained a BLEU score of 0.4047 (TEXT) and 0.3553(1-BEST) of the Challenge-EC task for our proposed method. On the other hand, we obtained a BLEU score of 0.3975(TEXT) and 0.3482(1-BEST) of the Challenge-EC task for a standard method. This means that our proposed method was effective for the Challenge-EC task. However, it was not effective for the BTECT-CE and Challenge-CE tasks. And our system was not good performance. For example, our system was the 7th place among 8 system for Challenge-EC task.

We present the TÜBİTAK-UEKAE statistical machine translation system that participated in the IWSLT 2008 evaluation campaign. Our system is based on the open-source phrase-based statistical machine translation software Moses. Additionally, phrase-table augmentation is applied to maximize source language coverage; lexical approximation is applied to replace out-of-vocabulary words with known words prior to decoding; and automatic punctuation insertion is improved. We describe the preprocessing and postprocessing steps and our training and decoding procedures. Results are presented on our participation in the classical Arabic-English and Chinese-English tasks as well as the new Chinese-Spanish direct and Chinese-English-Spanish pivot translation tasks.