We propose a novel data-driven rule-based preordering approach, which uses the tree information of multiple syntactic levels. This approach extend the tree-based reordering from one level into multiple levels, which has the capability to process more complicated reordering cases. We have conducted experiments in English-to-Chinese and Chinese-to-English translation directions. Our results show ...

Recently, deep models have shown tremendous improvements in neural machine translation (NMT). However, systems of this kind are computationally expensive and memory intensive. In paper, we take a natural step towards learning strong but light-weight NMT systems. We proposed novel group-permutation based knowledge distillation approach to compressing the Transformer model into shallow model. The...

MT systems are traditionally evaluated with different criteria, such as adequacy and fluency. Automatic evaluation scores are designed to match these quality parameters. In this paper we introduce a novel parameter – usability (or utility) of output, which was found to integrate both fluency and adequacy. We confronted two automated metrics, BLEU and LTV, with new data for which human evaluatio...

We present a dialectal Egyptian Arabic to English statistical machine translation system that leverages dialectal to Modern Standard Arabic (MSA) adaptation. In contrast to previous work, we first narrow down the gap between Egyptian and MSA by applying an automatic characterlevel transformational model that changes Egyptian to EG′, which looks similar to MSA. The transformations include morpho...

We present a novel technique for training translation models for statistical machine translation by aligning source sentences to their oracle-BLEU translations. In contrast to previous approaches which are constrained to phrase training, our method also allows the re-estimation of reordering models along with the translation model. Experiments show an improvement of up to 0.8 BLEU for our appro...

In this paper, we present five models for sentence realisation from a bag-of-words containing minimal syntactic information. It has a large variety of applications ranging from Machine Translation to Dialogue systems. Our models employ simple and efficient techniques based on n-gram Language modeling. We evaluated the models by comparing the synthesized sentences with reference sentences using ...

This paper presents an approach to the task of predicting an event description from a preceding sentence in a text. Our approach explores sequence-to-sequence learning using a bidirectional multi-layer recurrent neural network. Our approach substantially outperforms previous work in terms of the BLEU score on two datasets derived from WIKIHOW and DESCRIPT respectively. Since the BLEU score is n...

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 is the method proposed (based on the similarity wit...

In most statistical machine translation (SMT) systems, bilingual segments are extracted via word alignment. In this paper we compare alignments tuned directly according to alignment F-score and BLEU score in order to investigate the alignment characteristics that are helpful in translation. We report results for two different SMT systems (a phrase-based and an n-gram-based system) on Chinese to...