Identifying translation problems in English-Chinese sight translation

Abstract Translation problems have received considerable attention among translation process researchers and different research methods have been used to identify them. Findings are sometimes inconsistent, and as these studies have mainly studied translation between European languages, little research has been conducted to explore the issue concerning non-European languages. To fill this gap, the present study investigates problem triggers in English-Chinese sight translation in both directions (L1 and L2 translation). using eye-tracking data (Dragsted 2012). Results suggest that the type and number of translation problems encountered by the translators are different in L1 and L2 sight translation and that language-pair specificity is at play during the process, indicated by two identified Chinese-specific problem triggers, namely, back-sloping comma and head-final noun phrase.

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Translators’ Use of Digital Resources During Translation

HERMES - Journal of Language and Communication in Business ◽

10.7146/hjlcb.v0i56.97205 ◽

2017 ◽

pp. 71 ◽

Cited By ~ 2

Author(s):

Kristian Tangsgaard Hvelplund

Keyword(s):

Eye Movements ◽

Eye Tracking ◽

Cognitive Load ◽

Tracking Data ◽

Translation Process ◽

Digital Resources ◽

Use Of Resources ◽

Digital Resource

This paper presents the findings from a study on translators’ use of digital resources during the translation process. Eye tracking data and screen recording data from 18 professional translators are analysed in order to 1) examine how much time translators spend on digital resource consultation compared with translation drafting and translation revision, 2) examine how eye movements differ between translation drafting, revision and digital resource consultation and 3) investigate what types of digital resources are used by translators. The findings demonstrate that digital resource consultation constitutes a considerable amount of the translation process. The findings also show longer fixations and larger pupils during resource consultation, indicating heavier cognitive load, and finally the study identifies considerable variation in the use of resources between translators.

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Children's online use of word order and morphosyntactic markers in Tagalog thematic role assignment: an eye-tracking study

Journal of Child Language ◽

10.1017/s0305000919000618 ◽

2019 ◽

Vol 47 (3) ◽

pp. 533-555 ◽

Cited By ~ 1

Author(s):

Rowena GARCIA ◽

Jens ROESER ◽

Barbara HÖHLE

Keyword(s):

Eye Tracking ◽

Language Processing ◽

Noun Phrase ◽

Word Order ◽

Initial Conditions ◽

Tracking Data ◽

Thematic Role ◽

Role Assignment ◽

Heuristic Strategies

AbstractWe investigated whether Tagalog-speaking children incrementally interpret the first noun as the agent, even if verbal and nominal markers for assigning thematic roles are given early in Tagalog sentences. We asked five- and seven-year-old children and adult controls to select which of two pictures of reversible actions matched the sentence they heard, while their looks to the pictures were tracked. Accuracy and eye-tracking data showed that agent-initial sentences were easier to comprehend than patient-initial sentences, but the effect of word order was modulated by voice. Moreover, our eye-tracking data provided evidence that, by the first noun phrase, seven-year-old children looked more to the target in the agent-initial compared to the patient-initial conditions, but this word order advantage was no longer observed by the second noun phrase. The findings support language processing and acquisition models which emphasize the role of frequency in developing heuristic strategies (e.g., Chang, Dell, & Bock, 2006).

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Eye tracking and the translation process: reflections on the analysis and interpretation of eye-tracking data

MonTi Monografías de Traducción e Interpretación ◽

10.6035/monti.2014.ne1.6 ◽

2014 ◽

pp. 201-223 ◽

Cited By ~ 2

Author(s):

Kristian Tangsgaard Hvelplund

Keyword(s):

Eye Tracking ◽

Tracking Data ◽

Translation Process

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The Opposition of Surprisal and Semantic Similarity in the Prediction of Language Processing: Evidence from Eye-tracking Data

10.31234/osf.io/zypk9 ◽

2020 ◽

Author(s):

Kun Sun

Keyword(s):

Eye Tracking ◽

Semantic Similarity ◽

Cognitive Processing ◽

Language Processing ◽

Language Comprehension ◽

Word Processing ◽

Reading Time ◽

Computational Models ◽

Tracking Data ◽

Dynamic Approach

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

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