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• W3072195424 abstract "Systems that generate natural language text from abstract meaning representations such as AMR are typically evaluated using automatic surface matching metrics that compare the generated texts to reference texts from which the input meaning representations were constructed. We show that besides well-known issues from which such metrics suffer, an additional problem arises when applying these metrics for AMR-to-text evaluation, since an abstract meaning representation allows for numerous surface realizations. In this work we aim to alleviate these issues by proposing $mathcal{M}mathcal{F}_beta$, a decomposable metric that builds on two pillars. The first is the principle of meaning preservation $mathcal{M}$: it measures to what extent a given AMR can be reconstructed from the generated sentence using SOTA AMR parsers and applying (fine-grained) AMR evaluation metrics to measure the distance between the original and the reconstructed AMR. The second pillar builds on a principle of (grammatical) form $mathcal{F}$ that measures the linguistic quality of the generated text, which we implement using SOTA language models. In two extensive pilot studies we show that fulfillment of both principles offers benefits for AMR-to-text evaluation, including explainability of scores. Since $mathcal{M}mathcal{F}_beta$ does not necessarily rely on gold AMRs, it may extend to other text generation tasks." @default.
• W3072195424 created "2020-08-24" @default.
• W3072195424 creator A5010534476 @default.
• W3072195424 creator A5023977688 @default.
• W3072195424 date "2020-08-20" @default.
• W3072195424 modified "2023-09-26" @default.
• W3072195424 title "Towards a Decomposable Metric for Explainable Evaluation of Text Generation from AMR" @default.
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• W3072195424 hasPublicationYear "2020" @default.
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