Learning Language-Independent Representations of Verbs and Adjectives from Multimodal Retrieval

Victor Petren Bach Hansen; Anders Sogaard

doi:10.1109/SITIS.2018.00072

Learning Language-Independent Representations of Verbs and Adjectives from Multimodal Retrieval

Victor Petren Bach Hansen, Anders Sogaard

Department of Computer Science

Abstract

This paper presents a simple modification to previous work on learning cross-lingual, grounded word representations from image-word pairs that, unlike previous work, is robust across different parts of speech, e.g., able to find the translation of the adjective 'social' relying only on image features associated with its translation candidates. Our method does not rely on black-box image search engines or any direct cross-lingual supervision. We evaluate our approach on English-German and English-Japanese word alignment, as well as on existing English-German bilingual dictionary induction datasets.

Original language	English
Title of host publication	Proceedings - 14th International Conference on Signal-Image Technology and Internet Based Systems, SITIS
Number of pages	8
Publisher	IEEE
Publication date	2 Jul 2018
Pages	427-434
ISBN (Electronic)	978-1-5386-9385-8
DOIs	https://doi.org/10.1109/SITIS.2018.00072
Publication status	Published - 2 Jul 2018
Event	14th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2018 - Las Palmas de Gran Canaria, Spain Duration: 26 Nov 2018 → 29 Nov 2018

Conference

Conference	14th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2018
Country/Territory	Spain
City	Las Palmas de Gran Canaria
Period	26/11/2018 → 29/11/2018

Keywords

Computer vision
Cross-lingual learning
Distributional semantics
Multi-modal retrieval
Natural language processing

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10.1109/SITIS.2018.00072

Cite this

Learning Language-Independent Representations of Verbs and Adjectives from Multimodal Retrieval. / Hansen, Victor Petren Bach ; Sogaard, Anders.

Proceedings - 14th International Conference on Signal-Image Technology and Internet Based Systems, SITIS. IEEE, 2018. p. 427-434.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Hansen, VPB & Sogaard, A 2018, Learning Language-Independent Representations of Verbs and Adjectives from Multimodal Retrieval. in Proceedings - 14th International Conference on Signal-Image Technology and Internet Based Systems, SITIS. IEEE, pp. 427-434, 14th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2018, Las Palmas de Gran Canaria, Spain, 26/11/2018. https://doi.org/10.1109/SITIS.2018.00072

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title = "Learning Language-Independent Representations of Verbs and Adjectives from Multimodal Retrieval",

abstract = "This paper presents a simple modification to previous work on learning cross-lingual, grounded word representations from image-word pairs that, unlike previous work, is robust across different parts of speech, e.g., able to find the translation of the adjective 'social' relying only on image features associated with its translation candidates. Our method does not rely on black-box image search engines or any direct cross-lingual supervision. We evaluate our approach on English-German and English-Japanese word alignment, as well as on existing English-German bilingual dictionary induction datasets.",

keywords = "Computer vision, Cross-lingual learning, Distributional semantics, Multi-modal retrieval, Natural language processing",

author = "Hansen, {Victor Petren Bach} and Anders Sogaard",

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doi = "10.1109/SITIS.2018.00072",

language = "English",

pages = "427--434",

booktitle = "Proceedings - 14th International Conference on Signal-Image Technology and Internet Based Systems, SITIS",

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N2 - This paper presents a simple modification to previous work on learning cross-lingual, grounded word representations from image-word pairs that, unlike previous work, is robust across different parts of speech, e.g., able to find the translation of the adjective 'social' relying only on image features associated with its translation candidates. Our method does not rely on black-box image search engines or any direct cross-lingual supervision. We evaluate our approach on English-German and English-Japanese word alignment, as well as on existing English-German bilingual dictionary induction datasets.

AB - This paper presents a simple modification to previous work on learning cross-lingual, grounded word representations from image-word pairs that, unlike previous work, is robust across different parts of speech, e.g., able to find the translation of the adjective 'social' relying only on image features associated with its translation candidates. Our method does not rely on black-box image search engines or any direct cross-lingual supervision. We evaluate our approach on English-German and English-Japanese word alignment, as well as on existing English-German bilingual dictionary induction datasets.

KW - Computer vision

KW - Cross-lingual learning

KW - Distributional semantics

KW - Multi-modal retrieval

KW - Natural language processing

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DO - 10.1109/SITIS.2018.00072

M3 - Article in proceedings

AN - SCOPUS:85065904244

SP - 427

EP - 434

BT - Proceedings - 14th International Conference on Signal-Image Technology and Internet Based Systems, SITIS

PB - IEEE

T2 - 14th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2018

Y2 - 26 November 2018 through 29 November 2018

ER -

Learning Language-Independent Representations of Verbs and Adjectives from Multimodal Retrieval

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Keywords

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