Articles

MonoTrans2 is a translation system that com- bines machine translation (MT) with human computation using two crowds of monolin- gual source (Haitian Creole) and target (En- glish) speakers. We report on its use in the WMT 2011 Haitian Creole to English trans- lation task, showing that MonoTrans2 trans- lated 38% …...

This paper presents the LIU system for the WMT 2011 shared task for translation be- tween German and English. For English– German we attempted to improve the trans- lation tables with a combination of standard statistical word alignments and phrase-based word alignments. For German–English trans- lation we tried to make …...

This paper describes the statistical machine translation system submitted to the WMT11 Featured Translation Task, which involves translating Haitian Creole SMS messages into English. In our experiments we try to ad- dress the issue of noise in the training data, as well as the lack of parallel training data. Spelling …...

This paper describes the phrase-based SMT systems developed for our participation in the WMT11 Shared Translation Task. Translations for English↔German and English↔French were generated using a phrase-based translation system which is extended by additional models such as bilingual and fine-grained POS language models, POS-based reordering, lattice phrase extraction and discriminative …...

This paper describes the UPM system for translation task at the EMNLP 2011 workshop on statistical machine translation (http://www.statmt.org/wmt11/), and it has been used for both directions: Spanish-English and English-Spanish. This system is based on Moses with two new modules for pre and post processing the sentences. The main contribution …...

We present the Carnegie Mellon University Stat-XFER group submission to the WMT 2011 shared translation task. We built a hy- brid syntactic MT system for French–English using the Joshua decoder and an automati- cally acquired SCFG. New work for this year includes training data selection and grammar filtering. Expanded training …...

In this paper we describe our hybrid machine translation system with which we participated in the WMT11 shared translation task for the English→German language pair. Our system was able to outperform its RBMT baseline and turned out to be the best-scored participating system in the manual evaluation. To achieve this, …...

This paper describes the German-English translation system developed by the ARK re- search group at Carnegie Mellon University for the Sixth Workshop on Machine Trans- lation (WMT11). We present the results of several modeling and training improvements to our core hierarchical phrase-based trans- lation system, including: feature engineering to improve …...

We use target-side monolingual data to ex- tend the vocabulary of the translation model in statistical machine translation. This method called “reverse self-training” improves the de- coder’s ability to produce grammatically cor- rect translations into languages with morphol- ogy richer than the source language esp. in small-data setting. We empirically …...

We present the results we obtain using our RegMT system, which uses transductive re- gression techniques to learn mappings be- tween source and target features of given par- allel corpora and use these mappings to gen- erate machine translation outputs. Our train- ing instance selection methods perform fea- ture decay …...

We present a translation model enriched with shallow syntactic and semantic information about the source language. Base-phrase la- bels and semantic role labels are incorporated into an hierarchical model by creating shal- low semantic “trees”. Results show an in- crease in performance of up to 6% in BLEU scores for …...

This paper describes LIMSI’s submissions to the Sixth Workshop on Statistical Machine Translation. We report results for the French- English and German-English shared transla- tion tasks in both directions. Our systems use n-code, an open source Statistical Ma- chine Translation system based on bilingual n-grams. For the French-English task, we …...

This paper presents the Linguatec submission to the WMT 2011 sixth workshop on statistical machine translation. It describes the architecture of our machine translation system ‘Personal Translator’ (hereinafter also referred to as PT), developed by Linguatec, which is a rule-based translation system, enriched by statistical approaches. We participate for the …...

This work presents a simplified approach to bilingual topic modeling for language model adaptation by combining text in the source and target language into very short documents and performing Probabilistic Latent Semantic Analysis (PLSA) during model training. Dur- ing inference, documents containing only the source language can be used to …...

Most of the freely available parallel data to train the translation model of a statistical ma- chine translation system comes from very spe- cific sources (European parliament, United Nations, etc). Therefore, there is increasing interest in methods to perform an adaptation of the translation model. A popular approach is based …...

This paper presents the system we developed for the 2011 WMT Haitian Creole–English SMS featured translation task. Applying stan- dard statistical machine translation methods to noisy real-world SMS data in a low-density language setting such as Haitian Creole poses a unique set of challenges, which we attempt to address in …...

We present an empirical study of instance se- lection techniques for machine translation. In an active learning setting, instance selection minimizes the human effort by identifying the most informative sentences for transla- tion. In a transductive learning setting, se- lection of training instances relevant to the test set improves the …...

Statistical machine translation systems are normally optimised for a chosen gain func- tion (metric) by using MERT to find the best model weights. This algorithm suffers from stability problems and cannot scale beyond 20-30 features. We present an alternative al- gorithm for discriminative training of phrase- based MT systems, SampleRank, …...

In many languages the use of compound words is very productive. A common practice to reduce sparsity consists in splitting com- pounds in the training data. When this is done, the system incurs the risk of translating com- ponents in non-consecutive positions, or in the wrong order. Furthermore, a post-processing …...

Paraphrases are useful for statistical machine translation (SMT) and natural language pro- cessing tasks. Distributional paraphrase gen- eration is independent of parallel texts and syntactic parses, and hence is suitable also for resource-poor languages, but tends to erro- neously rank antonyms, trend-contrasting, and polarity-dissimilar candidates as good para- phrases. We …...