Articles

The quality of Arabic-English statistical ma- chine translation often suffers as a result of standard phrase-based SMT systems’ inabil- ity to perform long-range re-orderings, specif- ically those needed to translate VSO-ordered Arabic sentences. This problem is further ex- acerbated by the low performance of Arabic parsers on subject and subject …...

Languages with rich inflectional morphology pose a difficult challenge for statistical ma- chine translation. To address the problem of morphologically inconsistent output, we add unification-based constraints to the target-side of a string-to-tree model. By integrating con- straint evaluation into the decoding process, implausible hypotheses can be penalised or filtered out …...

We describe an approach for generating a ranked list of candidate document transla- tion pairs without the use of bilingual dic- tionary or machine translation system. We developed this approach as an initial, filter- ing step, for extracting parallel text from large, multilingual—but non-parallel— corpora. We represent bilingual documents in …...

We present KenLM, a library that imple- ments two data structures for efficient lan- guage model queries, reducing both time and memory costs. The PROBING data structure uses linear probing hash tables and is de- signed for speed. Compared with the widely- used SRILM, our PROBING model is 2.4 times …...

We consider using online language models for translating multiple streams which naturally arise on the Web. After establishing that us- ing just one stream can degrade translations on different domains, we present a series of simple approaches which tackle the problem of maintaining translation performance on all streams in small …...

This paper describes the JHU system combi- nation scheme used in WMT-11. The JHU system combination is based on confusion network alignment, and inherited the frame- work developed by (Karakos et al., 2008). We improved our core system combination al- gorithm by making use of TER-plus, which was originally designed …...

This paper describes the UZH system that was used for the WMT 2011 system combination shared task submission. We participated in the system combination task for the translation directions DE–EN and EN–DE. The system uses Moses as a backbone, with the outputs of the 2–3 best individual systems being inte- …...

BBN submitted system combination outputs for Czech-English, German-English, Spanish- English, and French-English language pairs. All combinations were based on confusion network decoding. The confusion networks were built using incremental hypothesis align- ment algorithm with flexible matching. novel bi-gram count feature, which can penal-A ize bi-grams not present in the input …...

RWTH participated in the System Combi- nation task of the Sixth Workshop on Sta- tistical Machine Translation (WMT 2011). For three language pairs, we combined 6 to 14 systems into a single consen- sus translation. A three-level meta- combination scheme combining six dif- ferent system combination setups with three different …...

This paper describes our submissions, cmu-heafield-combo, to the ten tracks of the 2011 Workshop on Machine Transla- tion’s system combination task. We show how the combination scheme operates by flexibly aligning system outputs then searching a space constructed from the alignments. Humans judged our combination the best on eight of …...

In past Evaluations for Machine Translation of European Languages, it could be shown that the translation performance of SMT systems can be increased by integrating a bilingual lan- guage model into a phrase-based SMT system. In the bilingual language model, target words with their aligned source words build the to- …...

This paper presents the submissions of the pat- tern recognition and human language technol- ogy (PRHLT) group to the system combina- tion task of the sixth workshop on statistical machine translation (WMT 2011). Each sub- missions is generated by a multi-system mini- mum Bayes risk (MBR) technique. Our tech- nique …...

This paper describes the development op- erated into MANY for the 2011 WMT system combination evaluation campaign. Hypotheses from French/English and En- glish/French MT systems were combined with a new version of MANY, an open source system combination software based on confusion networks decoding currently developed at LIUM. MANY has …...

The past few years have seen an increasing interest in using Amazon’s Mechanical Turk for purposes of collecting data and perform- ing annotation tasks. One such task is the mass evaluation of system output in a variety of tasks. In this paper, we present MAISE, a package that allows researchers …...

Automatic evaluation metrics are fundamen- tally important for Machine Translation, al- lowing comparison of systems performance and efficient training. Current evaluation met- rics fall into two classes: heuristic approaches, like BLEU, and those using supervised learn- ing trained on human judgement data. While many trained metrics provide a better match …...

We describe TINE, a new automatic evalua- tion metric for Machine Translation that aims at assessing segment-level adequacy. Lexical similarity and shallow-semantics are used as indicators of adequacy between machine and reference translations. The metric is based on the combination of a lexical matching com- ponent and an adequacy component. …...

We describe our submissions to the WMT11 shared MT evaluation task: MTeRater and MTeRater-Plus. Both are machine-learned metrics that use features from e-rater R⃝, an au- tomated essay scoring engine designed to as- sess writing proficiency. Despite using only features from e-rater and without comparing to translations, MTeRater achieves a …...

We propose the use of morphemes for auto- matic evaluation of machine translation out- put, and systematically investigate a set of F score and BLEU score based metrics calculated on words, morphemes and POS tags along with all corresponding combinations. Correlations between the new metrics and human judg- ments are …...

SemPOS is an automatic metric of machine translation quality for Czech and English fo- cused on content words. It correlates well with human judgments but it is computation- ally costly and hard to adapt to other lan- guages because it relies on a deep-syntactic analysis of the system output and …...

This paper describes Meteor 1.3, our submis- sion to the 2011 EMNLP Workshop on Sta- tistical Machine Translation automatic evalua- tion metric tasks. New metric features include improved text normalization, higher-precision paraphrase matching, and discrimination be- tween content and function words. We include Ranking and Adequacy versions of the metric …...