The main metric used for SMT systems evaluation an optimisation is BLEU score but this metric is questioned about its relevance to human evaluation. Some other metrics already exist but none of them are in perfect harmony with human evaluation. On the other hand, most evaluations use multiple metrics (BLEU, TER, METEOR, etc.). Systems can optimise toward other metrics than BLEU. But optimisatio...

Grammatical error correction (GEC) greatly benefits from large quantities of high-quality training data. However, the preparation a amount labelled data is time-consuming and prone to human errors. These issues have become major obstacles in GEC systems. Recently, performance English systems has drastically been enhanced by application deep neural networks that generate synthetic limited sample...

We describe our submission to the NIST Metrics for Machine Translation Challenge consisting of 4 metrics two versions of meteor, m-bleu and m-ter. We first give a brief description of Meteor . That is followed by descriptino of m-bleu and m-ter, enhanced versions of two other widely used metrics bleu and ter, which extend the exact word matching used in these metrics with the flexible matching ...

Abstract: Generating caption image automatically is one of the challenges in computer vision. This field can be very helpful many ways, for example search engines. Currently there are classification algorithms that we use to create a model. In this article, will compare performance between Resnet50 and InceptionV3 models text images. We 2000 (1800 train & 200 validation) data each has 5 cap...

In English-to-Japanese translation, BLEU (Papineni et al., 2002), the de facto standard evaluation metric for machine translation (MT), has very weak correlation with human judgments (Goto et al., 2011; Goto et al., 2013). Therefore, RIBES (Isozaki et al., 2010; Hirao et al., 2014) was proposed. RIBES measures similarity of the word order of a machine-translated sentence and that of a correspon...

We argue that the machine translation community is overly reliant on the Bleu machine translation evaluation metric. We show that an improved Bleu score is neither necessary nor sufficient for achieving an actual improvement in translation quality, and give two significant counterexamples to Bleu’s correlation with human judgments of quality. This offers new potential for research which was pre...

Bleu TX-III was isolated from Bothrops leucurus snake venom on one-step analytical chromatography reverse phase HPLC, was homogeneous on SDS-PAGE, and was confirmed by Q-Tof Ultima API ESI/MS (TOF MS mode) mass spectrometry in 14243.8 Da. Multiple alignments of Bleu TX-III show high degree of homology with basic PLA2 myotoxins from other Bothrops venoms. Our studies on local and systemic myotox...

Statistical post-editing has been shown in several studies to increase BLEU score for rule-based MT systems. However, previous studies have relied solely on BLEU and have not conducted further study to determine whether those gains indicated an increase in quality or in score alone. In this work we conduct a human evaluation of statistical post-edited output from a weak rule-based MT system, co...