MT-EQuAl (Machine Translation Errors, Quality, Alignment) is a toolkit for human assessment of Machine Translation (MT) output. MT-EQuAl implements three different tasks in an integrated environment: annotation of translation errors, translation quality rating (e.g. adequacy and fluency, relative ranking of alternative translations), and word alignment. The toolkit is webbased and multi-user, a...

Previous work on utilizing parse trees of source sentence in Attentional Neural Machine Translation was promising. However, current models suffer from a major drawback: they use only 1-best parse tree which may lead to translation mistakes due to parsing errors. In this paper we propose a forest-to-sequence Attentional Neural Machine Translation model which uses a forest instead of the 1-best t...

In this paper we show how different types of spelling errors influence the quality of machine translation. We also propose a method to evaluate the impact of spelling errors correction on translation quality without expensive manual work of providing reference translations.

In spoken language translation, it is crucial that an automatic speech recognition (ASR) system produces outputs that can be adequately translated by a statistical machine translation (SMT) system. While word error rate (WER) is the standard metric of ASR quality, the assumption that each ASR error type is weighted equally is violated in a SMT system that relies on structured input. In this pap...

OBJECTIVE Accurate, understandable public health information is important for ensuring the health of the nation. The large portion of the US population with Limited English Proficiency is best served by translations of public-health information into other languages. However, a large number of health departments and primary care clinics face significant barriers to fulfilling federal mandates to...

L1 interference in Lao students’ English writing and translation is widely believed to be a source of errors, but it has not been taken into account. As result, becomes interlanguage which considered as an error. This research aimed at investigating the errors translation. To collect data, tests were administered 213 (= 30%) EFL university students Department Language (DEL) Vientiane Capital, P...

Evaluation and error analysis of machine translation output are important but difficult tasks. In this work, we propose a novel method for obtaining more details about actual translation errors in the generated output by introducing the decomposition of Word Error Rate (WER) and Position independent word Error Rate (PER) over different Partof-Speech (POS) classes. Furthermore, we investigate tw...