Evaluating the Morphosyntactic Well-formedness of Generated Texts

• URL: http://arxiv.org/abs/2103.16590v1
• Date: Tue, 30 Mar 2021 18:02:58 GMT
• Title: Evaluating the Morphosyntactic Well-formedness of Generated Texts
• Authors: Adithya Pratapa, Antonios Anastasopoulos, Shruti Rijhwani, Aditi Chaudhary, David R. Mortensen, Graham Neubig, Yulia Tsvetkov
• Abstract summary: We propose L'AMBRE -- a metric to evaluate the morphosyntactic well-formedness of text. We show the effectiveness of our metric on the task of machine translation through a diachronic study of systems translating into morphologically-rich languages.
• Score: 88.20502652494521
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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