Learning distributional token representations from visual features


Broscheit, Samuel ; Gemulla, Rainer ; Keuper, Margret


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URL: https://madoc.bib.uni-mannheim.de/45649
Additional URL: http://aclweb.org/anthology/W18-3025
URN: urn:nbn:de:bsz:180-madoc-456495
Document Type: Conference or workshop publication
Year of publication: 2018
Book title: ACL 2018, Representation Learning for NLP : Proceedings of the Third Workshop : July 20, 2018 Melbourne, Australia
Page range: 187-194
Conference title: 3rd Workshop on Representation Learning for NL
Location of the conference venue: Melbourne, Australia
Date of the conference: 20.7.2018
Author/Publisher of the book
(only the first ones mentioned)
:
Augenstein, Isabelle
Place of publication: Stroudsburg, PA
Publishing house: Association for Computational Linguistics
ISBN: 978-1-948087-43-8
Publication language: English
Institution: School of Business Informatics and Mathematics > Praktische Informatik I (Gemulla 2014-)
License: CC BY 4.0
Subject: 004 Computer science, internet
Abstract: In this study, we compare token representations constructed from visual features (i.e., pixels) with standard lookup-based embeddings. Our goal is to gain insight about the challenges of encoding a text representation from low-level features, e.g. from characters or pixels. We focus on Chinese, which—as a logographic language—has properties that make a representation via visual features challenging and interesting. To train and evaluate different models for the token representation, we chose the task of character-based neural machine translation (NMT) from Chinese to English. We found that a token representation computed only from visual features can achieve competitive results to lookup embeddings. However, we also show different strengths and weaknesses in the models’ performance in a part-of- speech tagging task and also a semantic similarity task. In summary, we show that it is possible to achieve a text representation only from pixels. We hope that this is a useful stepping stone for future studies that exclusively rely on visual input, or aim at exploiting visual features of written language.

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Broscheit, Samuel ; Gemulla, Rainer ORCID: 0000-0003-2762-0050 ; Keuper, Margret Learning distributional token representations from visual features. Open Access Augenstein, Isabelle 187-194 In: ACL 2018, Representation Learning for NLP : Proceedings of the Third Workshop : July 20, 2018 Melbourne, Australia (2018) Stroudsburg, PA 3rd Workshop on Representation Learning for NL (Melbourne, Australia) [Conference or workshop publication]
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