Associating Gaze Information with Human Reading Strategies

Gaze behavior NLP technologies

Reading strategies

Text optimization

a t t r a c t i o n s t h r e a t e n i n g t h e i r v e r y e x i s t e n c e ?

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skipped

・Clues: word surface, POS, word length, frequency, etc.・Prediction with 0.95 similarity to observed data(for distribution across readers)・Regardless of individuality / unstableness general reading strategy

: saccade●: fixation

Previous label

: inputSentence The man will have to

: labelFixation/ skip

:POS DT

The

Length 0.07

-Trigram(2,3)

NNman

0.07

-

(2,3)

MDwill

0.06

0.38

(3,3)

VBhave

0.06

0.48

(3,3)

TOto

0.08

0.61

(3,3)Screen

-2 -1 0 1 2

:Position

Features of input sequence

:Surprisal

:

:

Word

Optimization of comma-placement

Prediction of word fixations/skips by readers

・For smoothing human readingLinguistic FeaturesCRF model

CRF model-based Comma Predictor

Gaze FeaturesHuman Annotation

Rule-based Comma Filter

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Comma Distribution for Readability

Input (Comma-less) Text