In this paper we present QE, an open source framework for machine translation quality estimation. The framework includes a feature extraction component and a machine learning component. We describe the architecture of the system and its use, focusing on the feature extraction component and on how to add new feature extractors. We also include experiments with features and learning algorithms available in the framework using the dataset of the WMT13 Quality Estimation shared task.

This paper describes the University of Sheffield's submission for the WMT16 Multimodal Machine Translation shared task, where we participated in Task 1 to develop German-to-English and Englishto-German statistical machine translation (SMT) systems in the domain of image descriptions. Our proposed systems are standard phrase-based SMT systems based on the Moses decoder, trained only on the provided data. We investigate how image features can be used to re-rank the n-best list produced by the SMT model, with the aim of improving performance by grounding the translations on images. Our submissions are able to outperform the strong, text-only baseline system for both directions.

No abstract

We describe our systems for the WMT14 Shared Task on Quality Estimation (subtasks 1.1, 1.2 and 1.3). Our submissions use the framework of Multi-task Gaussian Processes, where we combine multiple datasets in a multi-task setting. Due to the large size of our datasets we also experiment with Sparse Gaussian Processes, which aim to speed up training and prediction by providing sensible sparse approximations.

Matching a seller listed item to an appropriate product has become a fundamental and one of the most significant step for e-commerce platforms for product based experience. It has a huge impact on making the search effective, search engine optimization, providing product reviews and product price estimation etc. along with many other advantages for a better user experience. As significant and vital it has become, the challenge to tackle the complexity has become huge with the exponential growth of individual and business sellers trading millions of products everyday. We explored two approaches; classification based on shallow neural network and similarity based on deep siamese network. These models outperform the baseline by more than 5% in term of accuracy and are capable of extremely efficient training and inference.