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Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Anastasopoulos, Antonios
;
Chaudhary, Aditi
;
Mortensen, David R.
;
Neubig, Graham
;
Pratapa, Adithya
;
Rijhwani, Shruti
;
Tsvetkov, Yulia
. - : Underline Science Inc., 2021
Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.570/ 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. ...
Keyword:
Data Management System
;
Machine Learning
;
Machine translation
;
Natural Language Processing
URL:
https://dx.doi.org/10.48448/dzpt-d487
https://underline.io/lecture/37414-evaluating-the-morphosyntactic-well-formedness-of-generated-texts
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