This presentation considers deep temporal models in the brain. It builds on previous formulations of active inference to simulate behaviour and electrophysiological responses under deep (hierarchical) generative models of discrete state transitions. The deeply structured temporal aspect of these models means that evidence is accumulated over distinct temporal scales, enabling inferences about narratives (i.e., temporal scenes). We illustrate this behaviour in terms of Bayesian belief updating – and associated neuronal processes – to reproduce the epistemic foraging seen in reading. These simulations reproduce these sort of perisaccadic delay period activity and local field potentials seen empirically; including evidence accumulation and place cell activity. These simulations are presented as an example of how to use basic principles to constrain our understanding of system architectures in the brain – and the functional imperatives that may apply to neuronal networks.

Fatigue is physical and mental weariness caused by prolonged continuity of work and would undermine work performance. In translation studies, although fatigue is a confounding fac- tor previous experiments all try to control, its detection and measurement are largely ignored. To bridge this lacuna, this article recommends some subjective and objective approaches to measuring translation fatigue based on prior fatigue research. Meanwhile, as demotivation is believed to be an emotion that confounds its accurate measurements, a discussion on how to distinguish those two states is further conducted from theoretical and methodological perspec- tives. In doing so, this paper not only illuminates on how to measure two essential influencers of translation performance, but also offers some insights into the distinction of affective and physical states during translation process.

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.

Translation quality and efficiency are of great importance in the language services industry, which is why production duration and error counts are frequently investigated in Translation Process Research. However, a clear picture has not yet emerged as to how these two variables can be optimized or how they relate to one another. In the present study, data from multiple English-Japanese translation sessions is used to predict the number of errors per segment using source text and translator characteristics. An analysis utilizing zero-inflated generalized linear mixed effects models revealed that two source text characteristics (syntactic complexity and the proportion of long words) and three translator characteristics (years of experience, the time translators spent reading a source text before translating, and the time translators spent revising a translation) significantly influenced the number of errors. Furthermore, a lower proportion of long words per source text sentence and more training led to a significantly higher probability of error-free translation. Based on these results, combined with findings from a previous study on production duration, it is concluded that years of experience and the duration of the final revision phase are important factors that have a positive impact on translation efficiency and quality

Schaeffer et al. (2019) studied whether translation student’s error recognition processes dif- fered from those in professional translators. The stimuli consisted of complete texts, which contained errors of five kinds, following Mertin’s (2006) error typology. Translation students and professionals saw translations which contained errors produced by human translators and which had to be revised. Vardaro et al (2019) followed the same logic, but first determined the frequency of error types produced by the EU commission’s NMT system and then pre- sented single sentences containing errors based on the MQM typology. Participants in Vardaro et al (2019) were professional translators employed by the EU. For the current pur- pose, we present the results from a comparison between those 30 professionals in Vardaro et al (2019) and a group of 30 translation students. We presented the same materials as in Vardaro et al (2019) and tracked participants’ eye movements and keystrokes. Results show that translation competence interacts with how errors are recognized and corrected during post-editing. We discuss the results of this study in relation to current models of the transla- tion process by contrasting the predictions these make with the evidence from our study

In English to Japanese translation, a linear translation refers to a translation in which the word order of the source text is kept as unchanged as possible. Previous research suggests that linear translation reduces the cognitive effort for interpreters and translators compared to the non-linear case. In this study, we empirically tested whether this was also the case in a mon- olingual setting from the viewpoint of reception study. The difference between linear and non-linear translation was defined using Cross values, which quantify how much reordering was required in Japanese translation relative to an English source text. Reading effort was measured by the average total reading time on the target text. In a linear mixed-effects model analysis, variations in reading time per participant and text type were also considered random effects. The results revealed that the reading effort for the linear translation was smaller than that for the non-linear translation. In addition, the accuracy of text comprehension was also found to affect the reading time

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.

In view of the “predictive turn” in translation studies, empirical investigations of the translation process have shown increasing interest in studying features of the text which can predict translation efficiency and effort, especially using large-scale experimental data and rigorous statistical means. In this regard, a novel metric based on entropy (i.e., HTra) has been proposed and experimentally studied as a predictor variable. On the one hand, empirical studies show that HTra as a product-based metric can predict effort, and on the other, some conceptual analyses have provided theoretical justifications of entropy or entropy reduction as a description of translation from a process perspective. This paper continues the investigation of entropy, conceptually examining two ways of quantifying cognitive load, namely, shift of resource allocation and reduction of entropy, and argues that the former is represented by surprisal and ITra while the latter is represented by HTra. Both can be approximated via corpus-based means and used as potential predictors of effort. Empirical analyses were also conducted comparing the two metrics (i.e., HTra and ITra) in terms of their prediction of effort, which showed that ITra is a stronger predictor for TT production time while HTra is a stronger predictor for ST reading time. It is hoped that this would contribute to the exploration of dependable, theoretically justifiable means of predicting the effort involved in translation