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arXiv · 2103.16590

Evaluating the Morphosyntactic Well-formedness of Generated Texts

Adithya Pratapa·Antonios Anastasopoulos·Shruti Rijhwani·Aditi Chaudhary·David R. Mortensen·Graham Neubig·Yulia Tsvetkov
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Abstract

Text generation systems are ubiquitous in natural language processing applications. However, evaluation of these systems remains a challenge, especially in multilingual settings. In this paper, we propose L'AMBRE -- a metric to evaluate the morphosyntactic well-formedness of text using its dependency parse and morphosyntactic rules of the language. We present a way to automatically extract various rules governing morphosyntax directly from dependency treebanks. To tackle the noisy outputs from text generation systems, we propose a simple methodology to train robust parsers. We show the effectiveness of our metric on the task of machine translation through a diachronic study of systems translating into morphologically-rich languages.

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Adithya Pratapa, Antonios Anastasopoulos, Shruti Rijhwani, Aditi Chaudhary, David R. Mortensen, Graham Neubig, Yulia Tsvetkov. 2021-03-30. Evaluating the Morphosyntactic Well-formedness of Generated Texts. https://arxiv.org/abs/2103.16590

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