Abstract / Description of output
Recent work has shown that visual context improves cross-lingual sense disambiguation for nouns. We extend this line of work to the more challenging task of cross-lingual verb sense disambiguation, introducing the MultiSense dataset of 9,504 images annotated with English, German, and Spanish verbs. Each image in MultiSense is annotated with an English verb and its translation in German or Spanish. We show that cross-lingual verb sense disambiguation models benefit from visual context, compared to unimodal baselines. We also show that the verb sense predicted by our best disambiguation model can improve the results of a text-only machine translation system when used for a multimodal translation task.
Original language | English |
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Title of host publication | Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics |
Editors | Jill Burstein, Christy Doran, Thamar Solorio |
Place of Publication | Minneapolis, Minnesota |
Publisher | Association for Computational Linguistics |
Pages | 1998–2004 |
Number of pages | 7 |
Volume | 1 |
DOIs | |
Publication status | Published - 7 Jun 2019 |
Event | 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics - Minneapolis, United States Duration: 2 Jun 2019 → 7 Jun 2019 https://naacl2019.org/ |
Conference
Conference | 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics |
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Abbreviated title | NAACL-HLT 2019 |
Country/Territory | United States |
City | Minneapolis |
Period | 2/06/19 → 7/06/19 |
Internet address |