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        <title>Back-Translation as Strategy to Tackle the Lack of Corpus in Natural Language Generation from Semantic Representations</title>
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    <name type="personal">
        <namePart type="given">Marco</namePart>
        <namePart type="given">Antonio</namePart>
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    <name type="personal">
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    <abstract>This paper presents an exploratory study that aims to evaluate the usefulness of back-translation in Natural Language Generation (NLG) from semantic representations for non-English languages. Specifically, Abstract Meaning Representation and Brazilian Portuguese (BP) are chosen as semantic representation and language, respectively. Two methods (focused on Statistical and Neural Machine Translation) are evaluated on two datasets (one automatically generated and another one human-generated) to compare the performance in a real context. Also, several cuts according to quality measures are performed to evaluate the importance (or not) of the data quality in NLG. Results show that there are still many improvements to be made but this is a promising approach.</abstract>
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    <identifier type="doi">10.18653/v1/D19-6313</identifier>
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%0 Conference Proceedings
%T Back-Translation as Strategy to Tackle the Lack of Corpus in Natural Language Generation from Semantic Representations
%A Sobrevilla Cabezudo, Marco Antonio
%A Mille, Simon
%A Pardo, Thiago
%S Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)
%D 2019
%8 nov
%I Association for Computational Linguistics
%C Hong Kong, China
%F sobrevilla-cabezudo-etal-2019-back
%X This paper presents an exploratory study that aims to evaluate the usefulness of back-translation in Natural Language Generation (NLG) from semantic representations for non-English languages. Specifically, Abstract Meaning Representation and Brazilian Portuguese (BP) are chosen as semantic representation and language, respectively. Two methods (focused on Statistical and Neural Machine Translation) are evaluated on two datasets (one automatically generated and another one human-generated) to compare the performance in a real context. Also, several cuts according to quality measures are performed to evaluate the importance (or not) of the data quality in NLG. Results show that there are still many improvements to be made but this is a promising approach.
%R 10.18653/v1/D19-6313
%U https://aclanthology.org/D19-6313
%U https://doi.org/10.18653/v1/D19-6313
%P 94-103

Markdown (Informal)

[Back-Translation as Strategy to Tackle the Lack of Corpus in Natural Language Generation from Semantic Representations](https://aclanthology.org/D19-6313) (Sobrevilla Cabezudo et al., EMNLP 2019)

• Back-Translation as Strategy to Tackle the Lack of Corpus in Natural Language Generation from Semantic Representations (Sobrevilla Cabezudo et al., EMNLP 2019)

ACL

• Marco Antonio Sobrevilla Cabezudo, Simon Mille, and Thiago Pardo. 2019. Back-Translation as Strategy to Tackle the Lack of Corpus in Natural Language Generation from Semantic Representations. In Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019), pages 94–103, Hong Kong, China. Association for Computational Linguistics.