We introduce two simple improvements to the lexical weighting features of Koehn, Och, and Marcu (2003) for machine translation: one which smooths the probability of translating word f to word e by simplifying English morphology, and one which conditions it on the kind of training data that f and e co-occurred in. These new variations lead to improvements of up to +0.8 BLEU, with an average impr...

Some classic machine translation (MT) Evaluation methods, such as the bilingual evaluation understudy score (BLEU), have notably underperformed in evaluating translations for morphologically rich languages like Arabic. However, recent remarkable advancements domain of word vectors and sentence opened up new research avenues low-resource languages. This paper proposes a novel linguistic-based me...

This paper presents a look inside the ITC-irst largevocabulary SMT system developed for the NIST 2005 Chinese-to-English evaluation campaign. Experiments on official NIST test sets provide a thorough overview of the performance of the system, supplying information on how single components contribute to the global performance. The presented system exhibits performance comparable to that of the b...

We propose a novel extended translation model (ETM) to counteract some problems in phrase-based translation: The lack of translation context when using singleword phrases and uncaptured dependencies beyond phrase boundaries. The ETM operates on word-level and augments the IBM models by an additional bilingual word pair and a reordering operation. Its implementation in a phrase-based decoder int...

Recent research has shown that a balanced harmonic mean (F1 measure) of unigram precision and recall outperforms the widely used BLEU and NIST metrics for Machine Translation evaluation in terms of correlation with human judgments of translation quality. We show that significantly better correlations can be achieved by placing more weight on recall than on precision. While this may seem unexpec...

In this paper, we focus on the task of generating elliptic sentences. We extract from the data provided by the Surface Realisation (SR) Task (Belz et al., 2011) 2398 input whose corresponding output sentence contain an ellipsis. We show that 9% of the data contains an ellipsis and that both coverage and BLEU score markedly decrease for elliptic input (from 82.3% coverage for non-elliptic senten...

We introduce a lexicalized reordering model for hierarchical phrase-based machine translation. The model scores monotone, swap, and discontinuous phrase orientations in the manner of the one presented by Tillmann (2004). While this type of lexicalized reordering model is a valuable and widely-used component of standard phrase-based statistical machine translation systems (Koehn et al., 2007), i...

Machine translation is the process of translating text from one language to another. In this paper, Statistical Machine Translation is done on Assamese and English language by taking their respective parallel corpus. A statistical phrase based translation toolkit Moses is used here. To develop the language model and to align the words we used two another tools IRSTLM, GIZA respectively. BLEU sc...