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    <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.</abstract>
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%0 Conference Proceedings
%T Learning Distributional Token Representations from Visual Features
%A Broscheit, Samuel
%Y Augenstein, Isabelle
%Y Cao, Kris
%Y He, He
%Y Hill, Felix
%Y Gella, Spandana
%Y Kiros, Jamie
%Y Mei, Hongyuan
%Y Misra, Dipendra
%S Proceedings of the Third Workshop on Representation Learning for NLP
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F broscheit-2018-learning
%X 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.
%R 10.18653/v1/W18-3025
%U https://aclanthology.org/W18-3025
%U https://doi.org/10.18653/v1/W18-3025
%P 187-194

Markdown (Informal)

[Learning Distributional Token Representations from Visual Features](https://aclanthology.org/W18-3025) (Broscheit, RepL4NLP 2018)

• Learning Distributional Token Representations from Visual Features (Broscheit, RepL4NLP 2018)

ACL

• Samuel Broscheit. 2018. Learning Distributional Token Representations from Visual Features. In Proceedings of the Third Workshop on Representation Learning for NLP, pages 187–194, Melbourne, Australia. Association for Computational Linguistics.