Machine translation (MT) is being used by millions of people daily, and therefore evaluating the quality such systems an important task. While human expert evaluation MT output remains most accurate method, it not scalable any means. Automatic procedures that perform task Translation Quality Estimation (MT-QE) are typically trained on a large corpus source–target sentence pairs, which labeled w...

We describe QUEST, an open source framework for machine translation quality estimation. The framework allows the extraction of several quality indicators from source segments, their translations, external resources (corpora, language models, topic models, etc.), as well as language tools (parsers, part-of-speech tags, etc.). It also provides machine learning algorithms to build quality estimati...

Motion estimation and compensation is an essential part of existing video coding systems. The mesh-based motion estimation (MME) produces smoother motion field, better subjective quality (free from blocking artifacts), and higher peak signal-to-noise ratio (PSNR) in many cases, especially at low bitrate video communications, compared to the conventional block matching algorithm (BMA). Howev...

Machine Translation Quality Estimation (QE) is a task of predicting the quality machine translations without relying on any reference. Recently, predictor-estimator framework trains predictor as feature extractor, which leverages extra parallel corpora QE labels, achieving promising performance. However, we argue that there are gaps between and estimator in both data training objectives, preclu...

Every new tool gives humankind a capability or capabilities, as finds range of applications [...]