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            <namePart type="given">Kalika</namePart>
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    <abstract>The ability to bridge Question Generation (QG) and Question Answering (QA) across structured and unstructured modalities has the potential for aiding different NLP applications. One key application is in QA-based methods that have recently been shown to be useful for automatically evaluating Natural Language (NL) texts generated from Knowledge Graphs (KG). While methods have been proposed for QG-QA across these modalities, these efforts have been in English only; in this work, we bring multilinguality (Brazilian Portuguese and Russian) to multimodal (KG and NL) QG-QA. Using synthetic data generation and machine translation to produce QG-QA data that is aligned between graph and text, we are able to train multimodal, multi-task models that can perform multimodal QG and QA in Portuguese and Russian. We show that our approach outperforms a baseline which is derived from previous work on English and adapted to handle these two languages.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.918</identifier>
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%0 Conference Proceedings
%T Multilingual Generation and Answering of Questions from Texts and Knowledge Graphs
%A Han, Kelvin
%A Gardent, Claire
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F han-gardent-2023-multilingual
%X The ability to bridge Question Generation (QG) and Question Answering (QA) across structured and unstructured modalities has the potential for aiding different NLP applications. One key application is in QA-based methods that have recently been shown to be useful for automatically evaluating Natural Language (NL) texts generated from Knowledge Graphs (KG). While methods have been proposed for QG-QA across these modalities, these efforts have been in English only; in this work, we bring multilinguality (Brazilian Portuguese and Russian) to multimodal (KG and NL) QG-QA. Using synthetic data generation and machine translation to produce QG-QA data that is aligned between graph and text, we are able to train multimodal, multi-task models that can perform multimodal QG and QA in Portuguese and Russian. We show that our approach outperforms a baseline which is derived from previous work on English and adapted to handle these two languages.
%R 10.18653/v1/2023.findings-emnlp.918
%U https://aclanthology.org/2023.findings-emnlp.918
%U https://doi.org/10.18653/v1/2023.findings-emnlp.918
%P 13740-13756

Markdown (Informal)

[Multilingual Generation and Answering of Questions from Texts and Knowledge Graphs](https://aclanthology.org/2023.findings-emnlp.918) (Han & Gardent, Findings 2023)

• Multilingual Generation and Answering of Questions from Texts and Knowledge Graphs (Han & Gardent, Findings 2023)

ACL

• Kelvin Han and Claire Gardent. 2023. Multilingual Generation and Answering of Questions from Texts and Knowledge Graphs. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 13740–13756, Singapore. Association for Computational Linguistics.