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At a Glance: The Impact of Gaze Aggregation Views on Syntactic Tagging

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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: bi- nary 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 in- formative 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.
Original languageEnglish
Title of host publicationThe First Workshop Beyond Vision and LANguage: inTEgrating Real-World kNowledge : EMNLP-IJCNLP Workshop
Place of PublicationHong Kong
PublisherAssociation for Computational Linguistics
Publication date2019
Pages51–61
DOIs
Publication statusPublished - 2019

ID: 84192361