This research explores the interaction between human translators and Large Language Models (LLMs) during post-editing (PE). The study examines the impact of syntactic complexity on the PE processes and performance, specifically when working with the raw translation output generated by GPT-4. We selected four English source texts (STs) from previous American Translators Association (ATA) certification examinations. Each text is about 10 segments, with 250 words. GPT-4 was employed to translate the four STs from English into simplified Chinese. The empirical experiment simulated the authentic work environment of PE, using professional computer-assisted translation (CAT) tool, Trados. The experiment involved 46 participants with different levels of translation expertise (30 student translators and 16 expert translators), producing altogether 2162 segments of PE versions. We implemented five syntactic complexity metrics in the context of PE for quantitative analysis.

We report an experiment in which we use machine learning to validate the empirical objectivity of a novel annotation taxonomy for behavioral translation data. The HOF taxonomy defines three translation states according to which a human translator can be in a state of Orientation (O), Hesitation (H) or in a Flow state (F). We aim at validating the taxonomy based on a manually annotated dataset that consists of six English-Spanish translation sessions (approx 900 words) and 1813 HOF-annotated Activity Units (AUs). Two annotators annotated the data and obtain high average inter-annotator accuracy 0.76 (kappa 0.88). We train two classifiers, a Multi-layer Perceptron (MLP) and a Random Forest (RF) on the annotated data and tested on held-out data. The classifiers perform well on the annotated data and thus confirm the epistemological objectivity of the annotation taxonomy. Interestingly, inter-classifier accuracy scores are higher than between the two human annotators.

Translating via an intermediate pivot language is a common practice, but the impact of the pivot language on the quality of the final translation has not often been investigated. In order to compare the effect of different pivots, we back-translate 41 English source segments via vari- ous intermediate channels (Arabic, Chinese and monolingual paraphrasing) into English. We compare the 912 English back-translations of the 41 original English segments using manual evaluation, as well as COMET and various incarnations of BLEU. We compare human from- scratch back-translations with MT back-translations and monolingual paraphrasing. A varia- tion of BLEU (Cum-2) seems to better correlate with our manual evaluation than COMET and the conventional BLEU Cum-4, but a fine-grained qualitative analysis reveals that differences between different pivot languages (Arabic and Chinese) are not captured by the automatized TQA measures.

This study investigates the impact of translation briefs and search conditions on post-editing (PE) quality produced by participants with different levels of translation proficiency. We hired five Chinese student translators and seven Japanese professional translators to conduct full post-editing (FPE) and light post-editing (LPE), as described in the translation brief, while controlling two search conditions i.e., usage of a termbase (TB) and internet search (IS). Our results show that FPE versions of the final translations tend to have less errors than LPE ver- sions. The FPE translation brief improves participants’ performance on fluency as compared to LPE, whereas the search condition of TB helps to improve participants’ performance on accuracy as compared to IS. Our findings also indicate that the occurrences of fluency errors produced by experienced translators (i.e., the Japanese participants) are more in line with the specifications addressed in translation briefs, whereas the occurrences of accuracy errors pro- duced by inexperienced translators (i.e., our Chinese participants) depend more on the search conditions.

We look into English-German translation process data to analyse explicitation and implicitation phenomena of discourse connectives. For this, we use the database CRITT TPR-DB which contains translation process data with various features that elicit online translation behaviour. We explore the English-German part of the data for discourse connectives that are either omitted or inserted in the target, as well as cases when changing a weak signal to strong one, or the other way around. We determine several features that have an impact on cognitive effort during translation for explicitation and implicitation. Our results show that cognitive load caused by implicitation and explicitation may depend on the discourse connectives used, as well as on the strength and the type of the relations the connectives convey.

The CRITT (Center for Research and Innovation in Translation and Translation Technology) provides a Translation Process Research Database (TPR-DB) and a rich set of summary tables and tools that help to investigate translator behavior. In this paper, we describe a new tool in the TPR-DB that converts Trados Studio keylogging data (Qualitivity) into Translog-II format and adds the converted data to the CRITT TPR-DB. The tool is also able to synchronize with the output of various eye-trackers. We describe the components of the new TPR-DB tool and highlight some of the features that it produces in the TPR-DB tables.

Market pressure on translation productivity joined with technological innovation is likely to fragment and decontextualise translation jobs even more than is cur-rently the case. Many different translators increasingly work on one document at different places, collaboratively working in the cloud. This paper investigates the effect of decontextualised source texts on behaviour by comparing post-editing of sequentially ordered sentences with shuffled sentences from two different texts. The findings suggest that there is little or no effect of the decontextualised source texts on behaviour.

Speech-enabled interfaces have the potential to become one of the most efficient and ergonomic environments for human-computer interaction and for text production. However, not much research has been carried out to investigate in detail the processes and strategies involved in the different modes of text production. This paper introduces and evaluates a corpus of more than 55 hours of English-to-Japanese user activity data that were collected within the ENJA15 project, in which translators were observed while writing and speaking translations (translation dictation) and during machine translation post-editing. The transcription of the spoken data, keyboard logging and eye-tracking data were recorded with Translog-II, post-processed and integrated into the CRITT Translation Process Research-DB (TPR-DB), which is publicly available under a creative commons license. The paper presents the ENJA15 data as part of a large multilingual Chinese, Danish, German, Hindi and Spanish translation process data collection of more than 760 translation sessions. It compares the ENJA15 data with the other language pairs and reviews some of its particularities.

This paper describes the most recent dataset that has been added to the CRITT Translation Process Research Database (TPR-DB). Under the name CFT13, this new study contains user activity data (UAD) in the form of key-logging and eye-tracking collected during the second CasMaCat field trial in June 2013. The CFT13 is a publicly available resource featuring a number of simple and compound process and product units suited to investigate human-computer interaction while post-editing machine translation outputs.

This paper describes the field trial and subsequent evaluation of a post-editing workbench which is currently under development in the EU-funded CasMaCat project. Based on user evaluations of the initial prototype of the workbench, this second prototype of the workbench includes a number of interactive features designed to improve productivity and user satisfaction. Using CasMaCat’s own facilities for logging keystrokes and eye tracking, data were collected from nine post-editors in a professional setting. These data were then used to investigate the effects of the interactive features on productivity, quality, user satisfaction and cognitive load as reflected in the post-editors’ gaze activity. These quantitative results are combined with the qualitative results derived from user questionnaires and interviews conducted with all the participants.

This paper describes a pilot study with a computed-assisted translation workbench aiming at testing the integration of online and active learning features. We investigate the effect of these features on translation productivity, using interactive translation prediction (ITP) as a baseline. User activity data were collected from five beta testers using key-logging and eye-tracking. User feedback was also collected at the end of the experiments in the form of retrospective think-aloud protocols. We found that OL performs better than ITP, especially in terms of translation speed. In addition, AL provides better translation quality than ITP for the same levels of user effort. We plan to incorporate these features in the final version of the workbench.

The purpose of the current investigation is to predict post-editor profiles based on user behaviour and demographics using machine learning techniques to gain a better understanding of post-editor styles. Our study extracts process unit features from the CasMaCat LS14 database from the CRITT Translation Process Research Database (TPR-DB). The analysis has two main research goals: We create n-gram models based on user activity and part-of-speech sequences to automatically cluster post-editors, and we use discriminative classifier models to characterize post-editors based on a diverse range of translation process features. The classification and clustering of participants resulting from our study suggest this type of exploration could be used as a tool to develop new translation tool features or customization possibilities.

This paper presents a novel implementation of Translog-II. Translog-II is a Windows-oriented program to record and study reading and writing processes on a computer. In our research, it is an instrument to acquire objective, digital data of human translation processes. As their predecessors, Translog 2000 and Translog 2006, also Translog-II consists of two main components: Translog-II Supervisor and Translog-II User, which are used to create a project file, to run a text production experiments (a user reads, writes or translates a text) and to replay the session. Translog produces a log files which contains all user activity data of the reading, writing, or translation session, and which can be evaluated by external tools. While there is a large body of translation process research based on Translog, this paper gives an overview of the Translog-II functions and its data visualization options.

This paper introduces a publicly available database of recorded translation sessions for Translation Process Research (TPR). User activity data (UAD) of translators behavior was collected over the past 5 years in several translation studies with Translog 1 , a data acquisition software which logs keystrokes and gaze data during text reception and production. The database compiles this data into a consistent format which can be processed by various visualization and analysis tools.

We describe a set of experiments to explore statistical techniques for ranking and selecting the best translations in a graph of translation hypotheses. In a previous paper (Carl, 2007) we have described how the graph of hypotheses is generated through shallow transfer and chunk permutation rules, where nodes consist of vectors representing morpho-syntactic properties of words and phrases. This paper describes a number of methods to train statistical feature functions from some of the vector’s components. The feature functions are trained off-line on different types of text and their log-linear combination is then used to retrieve the best translation paths in the graph. We compare two language modelling toolkits, the CMU and the SRI toolkit and arrive at three results: 1) models of lemma-based feature functions produce better results than token-based models, 2) adding PoS-tag feature function to the lemma models improves the output and 3) weights for lexical translations are suited if the training material is similar to the texts to be translated.

In this paper we describe the METIS-II system and its evaluation on each of the language pairs: Dutch, German, Greek, and Spanish to English. The METIS-II system envisaged developing a data-driven approach in which no parallel corpus is required, and in which no full parser or extensive rule sets are needed. We describe evalution on a development test set and on a test set coming from Europarl, and compare our results with SYSTRAN. We also provide some further analysis, researching the impact of the number and source of the reference translations and analysing the results according to test text type. The results are expectably lower for the METIS system, but not at an unatainable distance from a mature system like SYSTRAN.

In this paper we describe a machine translation prototype in which we use only minimal resources for both the source and the target language. A shallow source language analysis, combined with a translation dictionary and a mapping system of source language phenomena into the target language and a target language corpus for generation are all the resources needed in the described system. Several approaches are presented.

Le nombre d’approches en traduction automatique s’est multiplié dans les dernières années. Il existe entre autres la traduction par règles, la traduction statistique et la traduction guidée par l’exemple. Dans cet article je decris les approches principales en traduction automatique. Je distingue les approches qui se basent sur des règles obtenues par l’inspection des approches qui se basent sur des exemples de traduction. La traduction guidée par l’exemple se caractérise par la phrase comme unité de traduction idéale. Une nouvelle traduction est génerée par analogie : seulement les parties qui changent par rapport à un ensemble de traductions connues sont adaptées, modifiées ou substituées. Je présente quelques techniques qui ont été utilisées pour ce faire. Je discuterai un système spécifique, EDGAR, plus en detail. Je démontrerai comment des textes traduits alignés peuvent être preparés en termes de compilation pour extraire des unités de traduction sous-phrastiques. Je présente des résultats en traduction Anglais -> Français produits avec le système EDGAR en les comparant avec ceux d’un système statistique.

In this paper we present a model for the future use of Machine Translation (MT) and Computer Assisted Translation. In order to accommodate the future needs in middle value translations, we discuss a number of MT techniques and architectures. We anticipate a hybrid environment that integrates data- and rule-driven approaches where translations will be routed through the available translation options and consumers will receive accurate information on the quality, pricing and time implications of their translation choice.

This paper presents an approach to extract invertible trans- lation examples from pre-aligned reference translations. The set of in- vertible translation examples is used in the Example-Based Machine Translation (EBMT) system EDGAR for translation. Invertible bilin- gual grammars eliminate translation ambiguities such that each source language parse tree maps into only one target language string. The trans- lation results of EDGAR are compared and combined with those of a translation memory (TM). It is shown that i) best translation results are achieved for the EBMT system when using a bilingual lexicon to sup- port the alignment process ii) TMs and EBMT-systems can be linked in a dynamical sequential manner and iii) the combined translation of TMs and EBMT is in any case better than each of the single system.

This paper describes an example-based machine translation (EBMT) system which relays on various knowledge resources. Morphologic analyses abstract the surface forms of the languages to be translated. A shallow syntactic rule formalism is used to percolate features in derivation trees. Translation examples serve the decomposition of the text to be translated and determine the transfer of lexical values into the target language. Translation templates determine the word order of the target language and the type of phrases (e.g. noun phrase, prepositional phase, ...) to be generated in the target language. An induction mechanism generalizes translation templates from translation examples. The paper outlines the basic idea underlying the EBMT system and investigates the possibilities and limits of the translation template induction process.

The paper reports on experiments which compare the translation outcome of three corpus-based MT systems, a string-based translation memory (STM), a lexeme-based translation memory (LTM) and the example-based machine translation (EBMT) system EDGAR. We use a fully automatic evaluation method to compare the outcome of each MT system and discuss the results. We investigate the benefits for the linkage of different MT strategies such as TMsystems and EBMT systems.