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

Essential Speech and Language Technology for Dutch: Results by the STEVIN-programme

Computational Linguistics; Germanic Languages; Artificial Intelligence (incl. Robotics); Computing Methodologie

Hybrid Arabic–French machine translation using syntactic re-ordering and morphological pre-processing

This is an accepted manuscript of an article published by Elsevier BV in Computer Speech & Language on 08/11/2014, available online: https://doi.org/10.1016/j.csl.2014.10.007 The accepted version of the publication may differ from the final published version.Arabic is a highly inflected language and a morpho-syntactically complex language with many differences compared to several languages that are heavily studied. It may thus require good pre-processing as it presents significant challenges for Natural Language Processing (NLP), specifically for Machine Translation (MT). This paper aims to examine how Statistical Machine Translation (SMT) can be improved using rule-based pre-processing and language analysis. We describe a hybrid translation approach coupling an Arabic–French statistical machine translation system using the Moses decoder with additional morphological rules that reduce the morphology of the source language (Arabic) to a level that makes it closer to that of the target language (French). Moreover, we introduce additional swapping rules for a structural matching between the source language and the target language. Two structural changes involving the positions of the pronouns and verbs in both the source and target languages have been attempted. The results show an improvement in the quality of translation and a gain in terms of BLEU score after introducing a pre-processing scheme for Arabic and applying these rules based on morphological variations and verb re-ordering (VS into SV constructions) in the source language (Arabic) according to their positions in the target language (French). Furthermore, a learning curve shows the improvement in terms on BLEU score under scarce- and large-resources conditions. The proposed approach is completed without increasing the amount of training data or radically changing the algorithms that can affect the translation or training engines.This paper is based upon work supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant number 356097-08.Published versio

Splitting Arabic Texts into Elementary Discourse Units

International audienceIn this article, we propose the first work that investigates the feasibility of Arabic discourse segmentation into elementary discourse units within the segmented discourse representation theory framework. We first describe our annotation scheme that defines a set of principles to guide the segmentation process. Two corpora have been annotated according to this scheme: elementary school textbooks and newspaper documents extracted from the syntactically annotated Arabic Treebank. Then, we propose a multiclass supervised learning approach that predicts nested units. Our approach uses a combination of punctuation, morphological, lexical, and shallow syntactic features. We investigate how each feature contributes to the learning process. We show that an extensive morphological analysis is crucial to achieve good results in both corpora. In addition, we show that adding chunks does not boost the performance of our system

Dutch compound splitting for bilingual terminology extraction

Compounds pose a problem for applications that rely on precise word alignments such as bilingual terminology extraction. We therefore developed a state-of-the-art hybrid compound splitter for Dutch that makes use of corpus frequency information and linguistic knowledge. Domain-adaptation techniques are used to combine large out-of-domain and dynamically compiled in-domain frequency lists. We perform an extensive intrinsic evaluation on a Gold Standard set of 50,000 Dutch compounds and a set of 5,000 Dutch compounds belonging to the automotive domain. We also propose a novel methodology for word alignment that makes use of the compound splitter. As compounds are not always translated compositionally, we train the word alignment models twice: a first time on the original data set and a second time on the data set in which the compounds are split into their component parts. The obtained word alignment points are then combined

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%