Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

A neural network architecture for detecting grammatical errors in statistical machine translation

In this paper we present a Neural Network (NN) architecture for detecting grammatical er- rors in Statistical Machine Translation (SMT) using monolingual morpho-syntactic word rep- resentations in combination with surface and syntactic context windows. We test our approach on two language pairs and two tasks, namely detecting grammatical errors and predicting over- all post-editing e ort. Our results show that this approach is not only able to accurately detect grammatical errors but it also performs well as a quality estimation system for predicting over- all post-editing e ort, which is characterised by all types of MT errors. Furthermore, we show that this approach is portable to other languages

A Morpho-Syntactic Error Analysis of Students‘ Writing at the State Madrasah Tsanawiyah of Sukoharjo

English is considered as an Indonesian students‘ foreign language which is not used frequently in their daily conversations. Since writing in English is a challenging experience for Indonesian students, they sometimes commit errors in grammar and sentence structures. Learners‘ errors were considered as the best sources to identify students‘ writing skills deficiency. They can be useful for teachers, learners, textbook providers, education system and so on. This descriptive qualitative research investigated the Indonesian EFL linguistics taxonomy of morpho-syntactic errors and the sources of the errors. The participants of the study were the third graders of the State Madrasah Tsanawiyah Sukoharjo. They were asked to write a descriptive text about their mother. After collecting the data and categorizing and identifying the erroneous areas in their work, the data were analysed using the linguistic taxonomy of errorscoined by Keshavarz (2006). The results of this study showed that the most frequent part of the students‘ errors based on morpho-syntactical errors was "errors due to a lack of concord" and the minimum frequency was found to be ―wrong word order‖. Then, the most common error viewed from the sources of the errors was overgeneralization error, and the minimum frequency was related to ―faulty categorization

Detection of semantic errors in Arabic texts

AbstractDetecting semantic errors in a text is still a challenging area of investigation. A lot of research has been done on lexical and syntactic errors while fewer studies have tackled semantic errors, as they are more difficult to treat. Compared to other languages, Arabic appears to be a special challenge for this problem. Because words are graphically very similar to each other, the risk of getting semantic errors in Arabic texts is bigger. Moreover, there are special cases and unique complexities for this language. This paper deals with the detection of semantic errors in Arabic texts but the approach we have adopted can also be applied for texts in other languages. It combines four contextual methods (using statistics and linguistic information) in order to decide about the semantic validity of a word in a sentence. We chose to implement our approach on a distributed architecture, namely, a Multi Agent System (MAS). The implemented system achieved a precision rate of about 90% and a recall rate of about 83%

Correcting Errors Using the Framework of Argumentation: Towards Generating Argumentative Correction Propositions from Error Annotation Schemas

PACLIC 23 / City University of Hong Kong / 3-5 December 200

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan languages

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan Languages publishes 17 papers that were presented at the conference organised in Dubrovnik, Croatia, 4-6 Octobre 2010