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        <title>At a Glance: The Impact of Gaze Aggregation Views on Syntactic Tagging</title>
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    <name type="personal">
        <namePart type="given">Sigrid</namePart>
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        <name type="personal">
            <namePart type="given">Aditya</namePart>
            <namePart type="family">Mogadala</namePart>
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            <namePart type="given">Dietrich</namePart>
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    <abstract>Readers’ eye movements used as part of the training signal have been shown to improve performance in a wide range of Natural Language Processing (NLP) tasks. Previous work uses gaze data either at the type level or at the token level and mostly from a single eye-tracking corpus. In this paper, we analyze type vs token-level integration options with eye tracking data from two corpora to inform two syntactic sequence labeling problems: binary phrase chunking and part-of-speech tagging. We show that using globally-aggregated measures that capture the central tendency or variability of gaze data is more beneficial than proposed local views which retain individual participant information. While gaze data is informative for supervised POS tagging, which complements previous findings on unsupervised POS induction, almost no improvement is obtained for binary phrase chunking, except for a single specific setup. Hence, caution is warranted when using gaze data as signal for NLP, as no single view is robust over tasks, modeling choice and gaze corpus.</abstract>
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%0 Conference Proceedings
%T At a Glance: The Impact of Gaze Aggregation Views on Syntactic Tagging
%A Klerke, Sigrid
%A Plank, Barbara
%Y Mogadala, Aditya
%Y Klakow, Dietrich
%Y Pezzelle, Sandro
%Y Moens, Marie-Francine
%S Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F klerke-plank-2019-glance
%X Readers’ eye movements used as part of the training signal have been shown to improve performance in a wide range of Natural Language Processing (NLP) tasks. Previous work uses gaze data either at the type level or at the token level and mostly from a single eye-tracking corpus. In this paper, we analyze type vs token-level integration options with eye tracking data from two corpora to inform two syntactic sequence labeling problems: binary phrase chunking and part-of-speech tagging. We show that using globally-aggregated measures that capture the central tendency or variability of gaze data is more beneficial than proposed local views which retain individual participant information. While gaze data is informative for supervised POS tagging, which complements previous findings on unsupervised POS induction, almost no improvement is obtained for binary phrase chunking, except for a single specific setup. Hence, caution is warranted when using gaze data as signal for NLP, as no single view is robust over tasks, modeling choice and gaze corpus.
%R 10.18653/v1/D19-6408
%U https://aclanthology.org/D19-6408
%U https://doi.org/10.18653/v1/D19-6408
%P 51-61

Markdown (Informal)

[At a Glance: The Impact of Gaze Aggregation Views on Syntactic Tagging](https://aclanthology.org/D19-6408) (Klerke & Plank, 2019)

• At a Glance: The Impact of Gaze Aggregation Views on Syntactic Tagging (Klerke & Plank, 2019)

ACL

• Sigrid Klerke and Barbara Plank. 2019. At a Glance: The Impact of Gaze Aggregation Views on Syntactic Tagging. In Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN), pages 51–61, Hong Kong, China. Association for Computational Linguistics.