This paper discusses the acceptability of Near Human Quality machine translation. It defines Near Human Quality and provides examples of cases where Near Human Quality machine translation is acceptable. The paper assumes that there are programs capable of generating Near Human Quality output after some customization. The purpose of this paper is to discuss the acceptability of Near Human Qualit...

Machine translation of patent documents is very important from a practical point of view. One of the key technologies for improving machine translation quality is the utilization of syntax. It is difficult to select the appropriate parser for patent translation because the effects of each parser on patent translation are not clear. This paper provides comparative evaluation of several state-of-...

Traditional 'one-size-fits-all' models are failing to meet businesses’ requirements. To support the growing demand for cost-effective translation, fine-grained control of quality is required, enabling fit-for-purpose content to be delivered at predictable quality and cost levels. This paper argues for customisable levels of quality, detailing the variables which can be altered to achieve a cert...

Well aware of the difficulties involved in integrating translating models and quality systems, we offer an overview of relevant developments in the field. Particular emphasis is placed on the pragmatic connotations of translation and on the methodological aspects of the Quality Paradigm, an approach to documentary translation that focuses activity on the target user.

Machine Translation (MT) is being deployed for a range of use-cases by millions of people on a daily basis. There should, therefore, be no doubt as to the utility of MT. However, not everyone is convinced that MT can be useful, especially as a productivity enhancer for human translators. In this chapter, I address this issue, describing how MT is currently deployed, how its output is evaluated ...

Word alignment is the basis of statistical machine translation. GIZA++ is a popular tool for producing word alignments and translation models. It uses a set of parameters that affect the quality of word alignments and translation models. These parameters exist to overcome some problems such as overfitting. This paper addresses the problem of tuning GIZA++ parameter for better translation qualit...

This study explores methods for developing a large scale Quality Estimation framework for Machine Translation. We expand existing resources for Quality Estimation across related languages by using different transfer learning methods. The transfer learning methods are: Transductive SVM, Label Propagation and Self-taught Learning. We use transfer learning methods on the available labelled dataset...

This work deals with the application of confidence measures within an interactivepredictive machine translation system in order to reduce human effort. If a small loss in translation quality can be tolerated for the sake of efficiency, user effort can be saved by interactively translating only those initial translations which the confidence measure classifies as incorrect. We apply confidence e...

Cross-language document summarization is a task of producing a summary in one language for a document set in a different language. Existing methods simply use machine translation for document translation or summary translation. However, current machine translation services are far from satisfactory, which results in that the quality of the cross-language summary is usually very poor, both in re...

Machine translation (MT) quality is evaluated through comparisons between MT outputs and the human translations (HT). Traditionally, this evaluation relies on form related features (e.g. lexicon and syntax) and ignores the transfer of meaning reflected in HT outputs. Instead, we evaluate the quality of MT outputs through meaning related features (e.g. polarity, subjectivity) with two experiment...