Document-level Machine Translation: Recent Progress and The Crux of Evaluation


Document-level Machine Translation: Recent Progress and The Crux of Evaluation


Rico Sennrich is an SNSF Professor at the University of Zurich working on natural language processing, with a special focus on machine translation and deep learning. His SNSF project focuses on better natural language understanding with multilingual resources and multi-task learning.

He is also a Lecturer at the University of Edinburgh. He is a member of the Machine Translation Group and the Edinburgh NLP Group.


betway必威体育娱乐,betway必威体育首页,betway必威官方homeMachine translation (MT) is still predominantly modelled and evaluated on the level of sentences, but neural methods have the potential to overcome this limitation and allow effective document-level modelling. However, practical challenges of document-level MT include the lack of suitable training data, the high computational cost of wider-context models, and low reward for "context-aware" translation in automatic metrics.

betway必威体育娱乐,betway必威体育首页,betway必威官方homeIn his talk, he will discuss recent neural architectures that take into account wider context and address computational and data bottlenecks in different ways, and their evaluation with test sets that are targeted towards discourse phenomena. While evaluation with automatic metrics such as BLEU is noisy and hard to interpret, he will show that targeted evaluation can guide the development of document-level system by highlighting the effects of various modelling decisions.

必威体育betway 必威体育官网下载 必威体育betway登录-官方网址注册 必威体育betway登录手机-APP全能版下载 必威体育官网下载-最新版APP 必威体育app手机版-官网app下载