@inproceedings{agarwal-etal-2020-machine,
title = "Machine Translation Aided Bilingual Data-to-Text Generation and Semantic Parsing",
author = "Agarwal, Oshin and
Kale, Mihir and
Ge, Heming and
Shakeri, Siamak and
Al-Rfou, Rami",
editor = "Castro Ferreira, Thiago and
Gardent, Claire and
Ilinykh, Nikolai and
van der Lee, Chris and
Mille, Simon and
Moussallem, Diego and
Shimorina, Anastasia",
booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
month = "12",
year = "2020",
address = "Dublin, Ireland (Virtual)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.webnlg-1.13",
pages = "125--130",
abstract = "We present a system for bilingual Data-ToText Generation and Semantic Parsing. We use a text-to-text generator to learn a single model that works for both languages on each of the tasks. The model is aided by machine translation during both pre-training and fine-tuning. We evaluate the system on WebNLG 2020 data 1 , which consists of RDF triples in English and natural language sentences in English and Russian for both the tasks. We achieve considerable gains over monolingual models, especially on unseen relations and Russian.",
}
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%0 Conference Proceedings
%T Machine Translation Aided Bilingual Data-to-Text Generation and Semantic Parsing
%A Agarwal, Oshin
%A Kale, Mihir
%A Ge, Heming
%A Shakeri, Siamak
%A Al-Rfou, Rami
%Y Castro Ferreira, Thiago
%Y Gardent, Claire
%Y Ilinykh, Nikolai
%Y van der Lee, Chris
%Y Mille, Simon
%Y Moussallem, Diego
%Y Shimorina, Anastasia
%S Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)
%D 2020
%8 December
%I Association for Computational Linguistics
%C Dublin, Ireland (Virtual)
%F agarwal-etal-2020-machine
%X We present a system for bilingual Data-ToText Generation and Semantic Parsing. We use a text-to-text generator to learn a single model that works for both languages on each of the tasks. The model is aided by machine translation during both pre-training and fine-tuning. We evaluate the system on WebNLG 2020 data 1 , which consists of RDF triples in English and natural language sentences in English and Russian for both the tasks. We achieve considerable gains over monolingual models, especially on unseen relations and Russian.
%U https://aclanthology.org/2020.webnlg-1.13
%P 125-130
Markdown (Informal)
[Machine Translation Aided Bilingual Data-to-Text Generation and Semantic Parsing](https://aclanthology.org/2020.webnlg-1.13) (Agarwal et al., WebNLG 2020)
ACL