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Computer Science > Computation and Language

arXiv:2103.16590 (cs)
[Submitted on 30 Mar 2021 (v1), last revised 9 Sep 2021 (this version, v2)]

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
View a PDF of the paper titled Evaluating the Morphosyntactic Well-formedness of Generated Texts, by Adithya Pratapa and 6 other authors
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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.
Comments: EMNLP 2021 camera-ready
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2103.16590 [cs.CL]
  (or arXiv:2103.16590v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2103.16590
arXiv-issued DOI via DataCite

Submission history

From: Adithya Pratapa [view email]
[v1] Tue, 30 Mar 2021 18:02:58 UTC (5,779 KB)
[v2] Thu, 9 Sep 2021 19:30:07 UTC (6,146 KB)
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Antonios Anastasopoulos
Shruti Rijhwani
Aditi Chaudhary
David R. Mortensen
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