Papers with natural reading

2 papers
GECO-MT: The Ghent Eye-tracking Corpus of Machine Translation (2022.lrec-1)

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Challenge: Despite improvements in machine translation output, remarkable differences can be observed when comparing machine translations (MT) and human translations.
Approach: They describe a corpus of eye movement data collected during natural reading of a human translation and a machine translation of . they use this corpus to investigate the effect of machine translation on the reading process and the effects of various error types on reading.
Outcome: The proposed corpus will be used in future research to investigate the effect of machine translation on the reading process and the effects of various error types on reading.
ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotation (2020.lrec-1)

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Challenge: a new dataset of eye-tracking and electroencephalography captures language understanding . eye movement data provides millisecond-accurate records of where humans look when reading .
Approach: They recorded and preprocessed eye-tracking and electroencephalography data during natural reading and during annotation.
Outcome: The study combines eye-tracking and electroencephalography to capture the reading process . the data can be used to evaluate state-of-the-art machine learning systems .

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