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    <abstract>We present an end-to-end neural approach to generate English sentences from formal meaning representations, Discourse Representation Structures (DRSs). We use a rather standard bi-LSTM sequence-to-sequence model, work with a linearized DRS input representation, and evaluate character-level and word-level decoders. We obtain very encouraging results in terms of reference-based automatic metrics such as BLEU. But because such metrics only evaluate the surface level of generated output, we develop a new metric, ROSE, that targets specific semantic phenomena. We do this with five DRS generation challenge sets focusing on tense, grammatical number, polarity, named entities and quantities. The aim of these challenge sets is to assess the neural generator’s systematicity and generalization to unseen inputs.</abstract>
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%0 Conference Proceedings
%T Evaluating Text Generation from Discourse Representation Structures
%A Wang, Chunliu
%A van Noord, Rik
%A Bisazza, Arianna
%A Bos, Johan
%Y Bosselut, Antoine
%Y Durmus, Esin
%Y Gangal, Varun Prashant
%Y Gehrmann, Sebastian
%Y Jernite, Yacine
%Y Perez-Beltrachini, Laura
%Y Shaikh, Samira
%Y Xu, Wei
%S Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F wang-etal-2021-evaluating
%X We present an end-to-end neural approach to generate English sentences from formal meaning representations, Discourse Representation Structures (DRSs). We use a rather standard bi-LSTM sequence-to-sequence model, work with a linearized DRS input representation, and evaluate character-level and word-level decoders. We obtain very encouraging results in terms of reference-based automatic metrics such as BLEU. But because such metrics only evaluate the surface level of generated output, we develop a new metric, ROSE, that targets specific semantic phenomena. We do this with five DRS generation challenge sets focusing on tense, grammatical number, polarity, named entities and quantities. The aim of these challenge sets is to assess the neural generator’s systematicity and generalization to unseen inputs.
%R 10.18653/v1/2021.gem-1.8
%U https://aclanthology.org/2021.gem-1.8
%U https://doi.org/10.18653/v1/2021.gem-1.8
%P 73-83

Markdown (Informal)

[Evaluating Text Generation from Discourse Representation Structures](https://aclanthology.org/2021.gem-1.8) (Wang et al., GEM 2021)

• Evaluating Text Generation from Discourse Representation Structures (Wang et al., GEM 2021)

ACL

• Chunliu Wang, Rik van Noord, Arianna Bisazza, and Johan Bos. 2021. Evaluating Text Generation from Discourse Representation Structures. In Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021), pages 73–83, Online. Association for Computational Linguistics.