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    <titleInfo>
        <title>FrameNet Annotations Alignment using Attention-based Machine Translation</title>
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    <name type="personal">
        <namePart type="given">Gabriel</namePart>
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            <title>Proceedings of the International FrameNet Workshop 2020: Towards a Global, Multilingual FrameNet</title>
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            <publisher>European Language Resources Association</publisher>
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                <placeTerm type="text">Marseille, France</placeTerm>
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        <identifier type="isbn">979-10-95546-58-0</identifier>
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    <abstract>This paper presents an approach to project FrameNet annotations into other languages using attention-based neural machine translation (NMT) models. The idea is to use an NMT encoder-decoder attention matrix to propose a word-to-word correspondence between the source and the target language. We combine this word alignment along with a set of simple rules to securely project the FrameNet annotations into the target language. We successfully implemented, evaluated and analyzed this technique on the English-to-French configuration. First, we analyze the obtained FrameNet lexicon qualitatively. Then, we use existing French FrameNet corpora to assert the quality of the translation. Finally, we trained a BERT-based FrameNet parser using the projected annotations and compared it to a BERT baseline. Results show substantial improvements in the French language, giving evidence to support that our approach could help to propagate FrameNet data-set on other languages.</abstract>
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        <date>2020-05</date>
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%0 Conference Proceedings
%T FrameNet Annotations Alignment using Attention-based Machine Translation
%A Marzinotto, Gabriel
%S Proceedings of the International FrameNet Workshop 2020: Towards a Global, Multilingual FrameNet
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-58-0
%G English
%F marzinotto-2020-framenet
%X This paper presents an approach to project FrameNet annotations into other languages using attention-based neural machine translation (NMT) models. The idea is to use an NMT encoder-decoder attention matrix to propose a word-to-word correspondence between the source and the target language. We combine this word alignment along with a set of simple rules to securely project the FrameNet annotations into the target language. We successfully implemented, evaluated and analyzed this technique on the English-to-French configuration. First, we analyze the obtained FrameNet lexicon qualitatively. Then, we use existing French FrameNet corpora to assert the quality of the translation. Finally, we trained a BERT-based FrameNet parser using the projected annotations and compared it to a BERT baseline. Results show substantial improvements in the French language, giving evidence to support that our approach could help to propagate FrameNet data-set on other languages.
%U https://aclanthology.org/2020.framenet-1.6
%P 41-47

Markdown (Informal)

[FrameNet Annotations Alignment using Attention-based Machine Translation](https://aclanthology.org/2020.framenet-1.6) (Marzinotto, Framenet 2020)

• FrameNet Annotations Alignment using Attention-based Machine Translation (Marzinotto, Framenet 2020)

ACL

• Gabriel Marzinotto. 2020. FrameNet Annotations Alignment using Attention-based Machine Translation. In Proceedings of the International FrameNet Workshop 2020: Towards a Global, Multilingual FrameNet, pages 41–47, Marseille, France. European Language Resources Association.