Isabel Lacruz’s research while affiliated with Kent State University and other places

Extraordinary advances in machine translation over the last three quarters of a century have profoundly affected many aspects of the translation profession. The widespread integration of adaptive “artificially intelligent” technologies has radically changed the way many translators think and work. In turn, groundbreaking empirical research has yielded new perspectives on the cognitive basis of the human translation process. Translation is in the throes of radical transition on both professional and academic levels. The game-changing introduction of neural machine translation engines almost a decade ago accelerated these transitions. This volume takes stock of the depth and breadth of resulting developments, highlighting the emerging rivalry of human and machine intelligence. The gathering and analysis of big data is a common thread that has given access to new insights in widely divergent areas, from literary translation to movie subtitling to consecutive interpreting to development of flexible and powerful new cognitive models of translation.

Extraordinary advances in machine translation over the last three quarters of a century have profoundly affected many aspects of the translation profession. The widespread integration of adaptive “artificially intelligent” technologies has radically changed the way many translators think and work. In turn, groundbreaking empirical research has yielded new perspectives on the cognitive basis of the human translation process. Translation is in the throes of radical transition on both professional and academic levels. The game-changing introduction of neural machine translation engines almost a decade ago accelerated these transitions. This volume takes stock of the depth and breadth of resulting developments, highlighting the emerging rivalry of human and machine intelligence. The gathering and analysis of big data is a common thread that has given access to new insights in widely divergent areas, from literary translation to movie subtitling to consecutive interpreting to development of flexible and powerful new cognitive models of translation.

Extraordinary advances in machine translation over the last three quarters of a century have profoundly affected many aspects of the translation profession. The widespread integration of adaptive “artificially intelligent” technologies has radically changed the way many translators think and work. In turn, groundbreaking empirical research has yielded new perspectives on the cognitive basis of the human translation process. Translation is in the throes of radical transition on both professional and academic levels. The game-changing introduction of neural machine translation engines almost a decade ago accelerated these transitions. This volume takes stock of the depth and breadth of resulting developments, highlighting the emerging rivalry of human and machine intelligence. The gathering and analysis of big data is a common thread that has given access to new insights in widely divergent areas, from literary translation to movie subtitling to consecutive interpreting to development of flexible and powerful new cognitive models of translation.

We examine the variability of Japanese-English and Japanese-Spanish translations at the level of bunsetsu (文節), the smallest coherent linguistic units that sound natural as part of Japanese sentences. These are equivalents of chunks or phrases in English, linguistic units generally larger than a word but smaller than a sentence. We measure variability by adapting the widely studied word translation entropy metric HTra to the context of bunsetsu. Word translation entropy has been shown to correlate with various behavioral measures of cognitive effort during translation between several language pairs. Word translation entropy values also correlate for translations of the same English source texts into several languages. Here, we extend the range of prior findings to translations from Japanese, a very different source language to English. We exhibit significant correlations of word translation entropy values in Japanese-English and Japanese-Spanish translations of bunsetsu from the same source texts. In line with prior observations on comparability of cognitive effort exerted in translations from English to closely related European languages, we also find comparable average word translation entropy values at the bunsetsu level for translations from Japanese to English and to Spanish. Nevertheless, we exhibit examples where there are large differences between entropy values for translations of specific types of bunsetsu into English and Spanish, relating these differences to general characteristics of the languages, such as the degree of dependence on context to infer meaning. We propose that in appropriate circumstances, different levels of cognitive effort during the translation process can be identified through differences in the variability of the translation product.

The translation process has often been described as a sequence of three steps, source text (ST) analysis, source-target transfer, and target text (TT) generation. We propose a radically different view, in which the human translation process consists of a hierarchy of interacting word and phrase translations systems which organize and integrate as dissipative structures. Activation of word (or phrase) translation systems is a non-selective subliminal process in the translator’s mind not restricted to one language. Depending on the entropy (i.e., the internal order) of the word translation systems, a human translator spends more or less time and energy during the translation process, which can be measured in the form of gaze patterns and production duration.

We introduce a notion of pause-word ratio computed using ranges of pause lengths rather than lower cutoffs for pause lengths. Standard pause-word ratios are indicators of cognitnve effort during different translation modalities.The pause range version allows for the study of how different types of pauses relate to the extent of cognitive effort and where it occurs in the translation process. In this article we focus on short monitoring pauses and how they relate to the cognitive effort involved in translation and post-editing for language pairs that are different in terms of semantic and syntactic remoteness. We use data from the CRITT TPR database, comparing translation and post-editing from English to Japanese and from English to Spanish, and study the interaction of pause-word ratio for short pauses ranging between 300 and 500ms with syntactic remoteness, measured by the CrossS feature, semantic remoteness, measured by HTra, and syntactic and semantic remoteness , measured by Literality. 1. Extended Abstract The Multiling subset of the CRITT TPR-DB database (Carl et al., 2016a) provides a large corpus of translation process data that facilitates comparisons across different languages and different translation modalities. It assembles user activity data obtained from translation tasks into several languages using a common set of six short English source texts. In particular, keystroke and eye tracking data were recorded during from-scratch translation sessions and during post-editing of machine translations. In this paper, we focus on translation and post-editing data from the BML12 study for English-to-Spanish (Mesa-Lao, 2014) and the ENJA15 study for English to Japanese (Carl et al., 2016b). By introducing refinements of the pause-word ratio measure of cognitive effort (Lacruz and Shreve, 2014) given by different ranges of pause lengths, we identify different patterns of cognitive effort for the two language pairs. These point to interesting differences in the translation process for when languages are more or less remote from each other that merit systematic investigation. In terms of language structure, Spanish is much closer to English than Japanese is to English. It is therefore to be expected that translation related tasks will be more effortful for English > Japanese than for English > Spanish. In addition, typed production of Japanese using an input method editor (IME) is more complex, and so more effortful, than typed production of Spanish. The expected extra effort involved in English > Japanese translation tasks as compared to English > Spanish translation tasks has been confirmed, for example in Carl et al. (2016b) and Schaeffer et al.

Judging quality in translation and interpreting and in the associated task of revision has a long and controversial history. We briefly comment on some aspects of this history to provide context for the contemporary perspectives on and investigations into quality assessment that are represented in this volume of Linguistica Antverpiensia, New Series: Themes in Translation Studies. A fundamental obstacle to progress is the lack of consensus about how to characterize high-quality translation or interpreting, let alone the identification of broadly accepted models for measuring translation or interpreting quality or the ability of translators or interpreters. The advent of machine translation and post-editing has focused attention on the very nature of quality: Is it proximity to a “gold standard” of perfection or is it characteristic of a product that simply serves its purpose well enough to satisfy the needs of the consumer? In other words, is quality something that should be measured and judged in absolute terms or in relative terms? Different philosophies of quality assessment reflect these dichotomies, with the absolutists seeking objective assessments based on detailed analyses of taxonomies of errors, whereas the relativists prefer a more holistic approach that is more sympathetic to subjective judgements. The contributors to this volume present a broad range of approaches to quality assessment in a variety of contexts. We describe their achievements and provide brief analyses through the lens of the framework above.